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Today

Senior Machine Learning Engineer

Intuit Mountain View, CA

Mountain View, CA

Today

$171k - $232k/yr Full time Ai Engineer Artificial Intelligence Automation

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Job Description

Build and productionize machine learning at scale with Intuit in Mountain View, CA (onsite). In this role, you’ll architect, code, optimize, and deploy ML models using modern tools and techniques. You’ll also help automate the lifecycle of ML solutions, monitor performance, and continuously improve outcomes for customers.

What you’ll do

Design and build systems that improve ML scalability, usability, and performance.
Collaborate cross-functionally with product managers, data scientists, and engineers to understand and refine machine learning and other algorithms.
Communicate results clearly to peers and leaders.
Evaluate state-of-the-art technologies and apply them to deliver customer benefits.
Work with multiple data sources, refining features from underlying data and building end-to-end pipelines with partners.

ML impact areas

Model productionalization: Partner with data scientists to take prototype models to customer-scale readiness.
- Increase training data where needed.
- Automate training and prediction.
- Orchestrate data for continuous prediction.
- Understand training data details and provide metrics to compare models.
Model enhancement: Improve prediction performance or reduce training time by working on existing codebases.
- Conduct exploratory performance-focused work or execute directed improvements based on ideas from data science partners.
Machine learning tools: Build project-specific or decoupled tooling to reduce pain points in the data science process.
- Speed up training.
- Simplify data processing.
- Strengthen data management tooling.

Key responsibilities in practice

You’ll be expected to help architect, implement, optimize, and deploy ML models using the latest industry tools. In addition, you’ll automate, deliver, monitor, and improve machine learning solutions. Success in this role depends on strengths across software development, systems engineering, data wrangling, feature engineering, software testing, and ML systems design.

Qualifications

Education: BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
Languages: Scala, Java, Python.
Computer science fundamentals: data structures, algorithms, performance complexity, and how computer architecture affects software performance (for example, I/O and memory tuning).
Software engineering fundamentals: version control systems (Git, GitHub), production-ready coding practices, and development workflows.
ML and data science tool familiarity: SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras.
ML concepts: training, validation, testing, and techniques such as classification, regression, and clustering.
Data processing and distributed systems: Spark, Hive, Flink.
Cloud: AWS and AWS SageMaker.
DevOps concepts: CI/CD.
Containers: Docker and Kubernetes.

Compensation

USD 171,000 - 231,500 per year.

Tech stack you’ll work with: Scala, Java, Python, SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras, Spark, Hive, Flink, AWS, AWS SageMaker, Docker, Kubernetes.

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Today

Principal AI Engineer (SDLC)

AT&T Dallas, TX

Dallas, TX

Today

Full time Backend Developer Agentic Ai Ai Agent

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Job Description

AT&T seeks a Principal AI Engineer to design, build, and deploy production-ready generative AI and enterprise applications across the full SDLC.

Responsibilities

Design, develop, and deploy AI-powered applications and platforms aligned to enterprise business objectives
Build generative AI solutions using Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agent-based workflows
Develop intelligent assistants including copilots, chatbots, and AI automation solutions to improve employee and customer experiences
Turn AI concepts, proofs of concept, and prototypes into production-ready software products
Design and build scalable APIs, microservices, and backend services powering AI-enabled applications
Integrate AI capabilities into existing enterprise platforms, business systems, and customer-facing applications
Develop secure, reliable, and reusable application components within modern software architectures
Partner with cross-functional teams to translate business requirements into technical solutions
Deploy and manage AI workloads across cloud environments including Azure and AWS
Implement containerized and cloud-native solutions using Kubernetes and modern orchestration technologies
Build and maintain enterprise-grade AI platforms designed for high-volume production workloads
Optimize performance, reliability, security, and scalability of AI services
Establish and maintain CI/CD pipelines for AI-enabled applications and services
Apply MLOps best practices for deployment, monitoring, testing, and lifecycle management of AI solutions
Support model integration, version management, governance, and operational excellence
Monitor production environments and continuously improve platform performance and user experience
Evaluate emerging AI technologies and identify opportunities for enterprise adoption
Collaborate with product managers, software engineers, data scientists, UX teams, and business stakeholders
Contribute to technical architecture decisions and AI engineering best practices
Drive continuous improvement across AI development methodologies and delivery frameworks

Requirements

Bachelor's degree in Computer Science, Software Engineering, Information Systems, or a related technical field
Experience developing enterprise applications using modern software engineering practices
Strong proficiency in Python and modern application development frameworks
Experience building RESTful APIs and microservices
Knowledge of generative AI technologies including LLMs and AI application architectures
Experience with cloud platforms such as Azure and/or AWS
Experience working within Agile and SDLC environments
Knowledge of CI/CD pipelines, DevOps practices, and software release management
Experience with containerization and orchestration technologies such as Kubernetes and Docker
Strong problem-solving, analytical, and collaboration skills

Technologies

Python
RESTful APIs
microservices
Large Language Models (LLMs)
Generative AI
Retrieval-Augmented Generation (RAG)
agent-based workflows
Azure
AWS
Kubernetes
Docker
CI/CD pipelines
DevOps
MLOps
Agile
SDLC
vector databases
prompt engineering
AI evaluation frameworks
MLOps platforms
machine learning deployment practices

Example projects

Enterprise generative AI chatbots and virtual assistants
AI-powered recruiting and talent acquisition solutions
Customer care AI assistants powered by GPT and LLM technologies
Retrieval-Augmented Generation (RAG) platforms
Multi-agent and agentic workflow automation systems
AI APIs and shared enterprise AI services
AI-enabled operational intelligence and analytics platforms

Example at AT&T

Build a Network Operations Copilot that leverages multiple LLMs
Integrate with trouble-ticket and operational systems
Provide intelligent outage recommendations
Surface network analytics and operational insights
Deliver a fully deployed, production-ready software solution

Preferred qualifications

Experience building RAG (Retrieval-Augmented Generation) solutions
Experience developing agentic AI workflows and autonomous AI systems
Knowledge of MLOps platforms and machine learning deployment practices
Experience integrating AI solutions into enterprise business systems
Familiarity with vector databases, prompt engineering, and AI evaluation frameworks
Experience developing scalable cloud-native AI applications
Exposure to enterprise security, governance, and responsible AI practices

Ideal background

Candidates may currently hold titles such as AI Engineer, Generative AI Engineer, Machine Learning Engineer, AI Solutions Engineer, AI Application Engineer, or AI Platform Engineer

Location and schedule

Dallas, TX onsite
Location listing includes: Atlanta, Georgia and Dallas, Texas
Weekly hours: 40
Time type: Regular

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Today

Sr Data Scientist

Xenith Solutions Reston, VA

Reston, VA

Today

$150k - $175k/yr Full time Senior Analytics Business Analytics

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Job Description

The Sr Data Scientist role at Xenith Solutions applies statistical analysis, data processing, and visualization to turn complex datasets into actionable insights that support data-driven decision-making. The position includes building models and dashboards, working with structured and unstructured data, and partnering with stakeholders while upholding data integrity, security, and governance/compliance.

Key Responsibilities

Use statistical analysis, data processing, and visualization techniques to extract insights from complex datasets, enabling data-driven decision-making.
Clean, transform, and analyze both structured and unstructured data to surface trends, patterns, and key performance indicators.
Develop data models, optimize queries, and produce dashboards or reports to communicate findings effectively.
Collaborate with stakeholders to understand business or mission objectives and align data solutions with organizational goals.
Maintain data integrity by following security protocols and applying best practices for data governance and compliance.

Required Qualifications

Proficiency in one or more of: C++, Python, or Java.
Proficiency with Docker (or Podman) and Helm for application packaging and deployment.
Experience deploying and configuring applications within Kubernetes, including defining ingress rules for traffic routing, using Cert manager for SSL/TLS, and interfacing with persistent storage (e.g., Longhorn).
Proficiency with Git-based workflows using Bitbucket, GitHub, or GitLab.
Familiarity with the Atlassian Suite including Jira, Confluence, and Bitbucket.
Experience with Agile software development practices such as build automation, testing automation, pair programming, and code management.
Experience supporting configuration management and software release processes.
Experience with Linux (e.g., RHEL) and comfort working from the command line.

Preferred Qualifications

Experience managing the full lifecycle of distributed microservices in a production environment.
Experience building scalable RESTful APIs using frameworks such as FastAPI, Flask, or Spring Boot.
Experience implementing asynchronous, event-driven architectures using Kafka or RabbitMQ.
Experience optimizing PostgreSQL for high-volume data and using Redis for distributed caching and state management.
A deep understanding of cloud-native principles with a focus on scalability, high availability, and fault tolerance.

Technologies

Python, R, SQL, C++, Java
Docker, Podman, Helm, Kubernetes, Cert manager, Longhorn
Git, Bitbucket, GitHub, GitLab
Jira, Confluence, Atlassian Suite
Linux, RHEL
FastAPI, Flask, Spring Boot
Kafka, RabbitMQ
PostgreSQL, Redis

Location and Compensation

Location: Reston, VA (onsite).
Salary: USD 150,000 - 175,000 per year.

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Today

AI Engineer - FDE Software Engineering Sr. Manager

Accenture New York, NY

New York, NY

Today

$123k - $302k/yr Full time Backend Developer Manager Agentic Ai

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Job Description

Accenture is seeking an AI Native Engineer to embed with clients and help design, build, and deploy agentic workflows and AI-native systems in enterprise environments. The work spans end-to-end agent architecture, platform integration, and cloud-native engineering, with hands-on client engagement through POCs and code-with sessions.

What you’ll do

Design and engineer enterprise-ready AI agents including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Build abstraction layers across AI providers such as Anthropic, Google, OpenAI, and others to support seamless integration and enablement.
Use containerization and cloud-native patterns including Kubernetes, Docker, microservices, serverless, and event-driven architectures, along with CI/CD and observability to deliver scalable AI-native systems.
Tailor and deploy agentic applications across verticals such as finance, healthcare, and retail, addressing domain-specific processes through intelligent automation.
Run design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders and support trust and adoption.
Define and apply key metrics, test harnesses, and evaluation plans to assess accuracy, latency, safety, and cost effectiveness.
Create reusable patterns, documentation, and best practices that can influence internal assets and client roadmaps.

Requirements

10+ years of end-to-end software engineering experience and SDLC expertise.
8+ years with programming in Python, Java, or equivalent, plus familiarity with evaluation tooling, logging, monitoring, and agent observability.
8+ years deploying to production, including CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
8+ years in client communication and collaboration, including leading technical workshops and delivering under ambiguity.
3+ years engineering cloud-native systems, including APIs, microservices, containerization, and serverless.
3+ years working with AI platforms such as OpenAI and Claude, and open-source models, including building abstraction layers to manage multi-provider pipelines.
2+ years hands-on experience designing and delivering Agentic AI solutions.
1.5+ years experience with semantic models, ontologies, knowledge graphs, or enterprise knowledge.
1.5+ years experience using coding agents and AI-assisted software development.
1+ year expertise designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Bachelor’s degree or equivalent (minimum 12 years work experience). If Associate Degree, must have minimum 6 years work experience.

Technologies

Python, Java, Anthropic, Google, OpenAI, Claude
Kubernetes, Docker
Microservices, serverless, event-driven architectures
CI/CD, observability, Terraform, Helm
APIs, open-source models
Semantic models, ontologies, knowledge graphs
RAG, AI agents

Location and role details

Location: New York, NY (onsite)
Compensation: USD 122,700 - 302,400 per year
Travel: Travel may be required for this role, varying from 0 to 100% based on business need and client requirements.

Benefits

Medical, dental, vision, life, and long-term disability coverage
401(k) plan
Bonus opportunities
Paid holidays
Paid time off

Bonus points

Experience as an Agentic AI Engineer in an enterprise environment
Additional AI certifications or agentic tool experience
Experience defining or working with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures
Understanding of the AI-native paradigm, blending cloud-native with generative model architectures optimized for performance, modularity, and efficiency
Delivered solutions across multiple industries (for example, finance and healthcare) by tailoring agentic workflows to industry needs

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Today

Sr Data Scientist - AI Engineer

Home Depot / THD Atlanta, GA

Atlanta, GA

Today

Full time Ai Ml Artificial Intelligence Automation

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Job Description

Home Depot’s THD data science team is looking for a Senior Data Scientist to build production-ready AI capabilities that connect enterprise data to real workflows. This role focuses on establishing MLOps/LLMOps/AIOps operational frameworks and developing agentic AI solutions that can reason over data and orchestrate models into end-to-end execution.

What you will do

You will take ownership of solution development across advanced analytics, from designing models for large datasets to selecting and applying analytical methodologies that support business outcomes. A major part of the work includes ensuring outputs are executed with strong efficiency and quality, then translating results into clear recommendations for technical and non-technical leaders.

Design and develop algorithms and models that generate business insights from large datasets
Apply advanced analytical methodologies, including appropriate selection, utilization, and interpretation
Communicate insights and recommendation impacts to drive alignment and implementation
Prepare reports, updates, and presentations on progress for projects and solutions

You will also collaborate across teams to shape project direction and execution. This includes helping set goals, guiding prioritization, and supporting team quality through direction and coaching of more junior roles. The role includes coordination with managers and teams on workload and resource distribution, and involvement in recruiting and hiring efforts for the group.

Work with project teams and business partners to determine project goals
Provide direction on prioritization and ensure quality of work
Mentor and coach more junior roles to strengthen technical competencies
Collaborate with managers and teams on distribution of workload and resources
Support recruiting and hiring efforts for the team

Additional responsibilities include leveraging business context to improve solution approaches and building trusted relationships with internal customers and cross-functional teams. You will also support partner enablement by providing general education on advanced analytics, aligned with your understanding of IT needs required to solve business problems and create data science advantage.

Leverage business knowledge to shape the solution approach
Develop trust and collaboration with internal customers and cross-functional teams
Educate technical and non-technical partners on advanced analytics
Apply a deep understanding of IT needs to help the team tackle business problems
Actively pursue new business opportunities where data science can drive competitive advantage

On the technical side, you will continue exploring emerging capabilities and improving how solutions are delivered. This includes developing replicable, well-documented solutions (such as codified data products, project documentation, and process flowcharts), defining best practices for data analysis and model productionalization, and contributing to a reusable library of algorithms with clear documentation.

Stay current on key data science developments, technical skill sets, and additional data sources
Improve data science and analytics through replicable solutions for future reuse
Define best practices and develop a clear vision for productionalization
Contribute to a library of reusable algorithms with documented code

Requirements

Must be eighteen years of age or older
Must be legally permitted to work in the United States
Minimum 6+ years of experience in data science, machine learning engineering, AI engineering, software engineering, or MLOps, focused on production-ready AI solutions
2+ years of hands-on experience developing, deploying, evaluating, or supporting GenAI, LLM-based, or agentic AI solutions
Experience building agentic AI systems and multi-step workflows using tool calling, reasoning and planning, state and memory management, structured outputs, RAG/retrieval systems, embeddings, and API integration
Strong software engineering skills: Python, SQL, automated testing, containerization (Docker/Kubernetes), and cloud deployment (GCP preferred), with collaboration across Engineering, DevOps, and SRE partners
Expertise in foundational MLOps/LLMOps/AIOps practices: CI/CD automation, model/agent registries, versioning, automated testing, monitoring, automated retraining, rollback strategies, release management, and production support
Expertise in AI observability and governance: tracing, telemetry, automated alerting, anomaly detection, incident triage, eval harnesses, safety guardrails, model explainability, approval paths, fallback mechanisms, tool-use auditing, and cost/latency monitoring, including human-in-the-loop controls
Hands-on experience with agent orchestration frameworks, structured agent communication protocols (MCP, A2A), and Infrastructure-as-Code (IaC)
Ability to prototype lightweight tools or interfaces, evaluate technical feasibility, and document reusable architectural patterns
Continuous learning agility for emerging AI architectures, protocols, and operating models
Domain experience in merchandising, retail, ecommerce, supply chain, assortment planning, or space planning

Tools and technologies

Python, SQL
Docker, Kubernetes
GCP
CI/CD, CI/CD automation
RAG, retrieval systems, embeddings
API integration
MLOps, LLMOps, AIOps
IaC, MCP, A2A

Role details

Location: Atlanta, GA (onsite)
Minimum years of work experience: 5
Travel: typically requires overnight travel less than 10% of the time
Education: bachelor’s degree program or equivalent degree in a field of study related to the job
Reporting: this position reports to manager or above; 0 direct reports

Working conditions

Most of the time is spent sitting in a comfortable position, with frequent opportunity to move about
On rare occasions, there may be a need to move or lift light articles
Located in a comfortable indoor area; any unpleasant conditions would be infrequent and not objectionable

Competencies

Attracts Top Talent
Business Insight
Collaborates
Communicates Effectively
Cultivates Innovation
Customer Focus
Develops Talent
Directs Work
Drives Results
Nimble Learning
Optimizes Work Processes
Self-Development

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Today

AI Engineer - Entry to Expert Level

US National Security Agency/Central Security Service Fort Meade, MD

Fort Meade, MD

Today

$87k - $197k/yr Full time Ai Engineer Ai Ml Artificial Intelligence

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Job Description

At the US National Security Agency/Central Security Service, this role focuses on designing and implementing mission-critical AI systems that support intelligence collection, processing, and reporting. You will help build, deploy, and maintain AI capabilities across the full lifecycle, from training and infrastructure through deployment, performance monitoring, and continuous optimization.

This position is based in Fort Meade, MD (onsite), supporting critical national security work. The salary range is USD 87,362 - 197,200 per year, with pay level determined by education and relevant experience.

What you will do

Lead or contribute to cross-functional teams to develop and operationalize AI solutions for challenging, mission-focused problems.
Design, develop, deploy, and maintain end-to-end AI workflows covering model training, inference, and performance monitoring.
Adapt and integrate AI model architectures including computer vision systems, natural language processing, audio/speech processing, large language models (LLMs), and multi-modal frameworks.
Monitor and maintain AI products by identifying performance degradation and computational inefficiency, then apply regular fine-tuning to maintain alignment with evolving mission and organizational needs.
Stay current with AI research and incorporate emerging techniques into intelligence applications.
Test and evaluate AI solutions against mission requirements and provide actionable recommendations.

Entry and experience levels

Entry: For Computer Science or Engineering, an Associate’s degree plus 2 years of relevant experience, or a Bachelor’s degree with no experience, or a Master’s degree with no experience.
Entry: For Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical/Biological Sciences, an Associate’s degree plus 3 years, or a Bachelor’s degree plus 1 year.
Full Performance: For Computer Science or Engineering, Associate’s + 5 years, or Bachelor’s + 3 years, or Master’s + 1 year, or Doctoral + no experience.
Full Performance: For Information Systems, Information Technology, Mathematics, Applied Mathematics, Statistics, Applied Statistics, Operations Research, Artificial Intelligence, Data Science, or Physical/Biological Sciences, Associate’s + 5 years, or Bachelor’s + 3 years, or Master’s + 1 year, or Doctoral + 1 year.
Senior: Associate’s + 8 years, or Bachelor’s + 6 years, or Master’s + 4 years, or Doctoral + 2 years.
Senior: Degree must be in one of the listed fields for Senior qualification.
Expert: Associate’s + 11 years, or Bachelor’s + 9 years, or Master’s + 7 years, or Doctoral + 5 years.
Expert: Degree must be in one of the listed fields for Expert qualification.
Expert: Relevant experience must include three or more from: implementing production scale AI/ML solutions; distributed model training; distributed AI/ML systems; AI/ML performance monitoring; platform engineering; cloud engineering; developing deep learning models; sustaining/maintaining AI/ML models; implementing AI/ML algorithms; AI/ML model development and deployment; DevOps; MLOps; cloud infrastructure management; software engineering; automated testing; or containerization.
Expert: Additionally, you must have experience serving as an AI Project Team Leader/model owner.
Education must be accredited by an institution recognized by the U.S. Department of Education.

Technologies you may use

Frameworks and languages: PyTorch, TensorFlow, JAX, Python, C++, Java, Scala, TypeScript
Data and distributed processing: SQL, NoSQL, Spark, Dask, Ray
Cloud, containers, and orchestration: AWS, Azure, GCP, Docker, Kubernetes
Collaboration tools: Git

Pay, schedule, and appointment details

Appointment type: Permanent
Federal service type: Excepted Service
Pay scale & grade: GG 7 - 15
Work schedule: Full-time, Monday - Friday, with an 8-hour/day requirement between 6:00 a.m. and 6:00 p.m. (flexible)
Travel: Occasional travel may be expected
Relocation expenses: Reimbursed if you qualify under agency policy
Telework/remote: Telework eligible: No; Remote job: No

Security and compliance

Security clearance: Top Secret
Position sensitivity: Critical-Sensitive (CS)/High Risk; background check type: National security
Drug testing: All applicants and employees are subject to random drug testing in accordance with Executive Order 12564.
Financial disclosure: Required
Drug test: No (as listed in overview); random drug testing still applies per the requirement statement

How you will be evaluated

Specialized skills in deep learning frameworks (PyTorch, TensorFlow, JAX)
Experience with model training, fine-tuning, and optimization techniques
Experience with computer vision, NLP, speech/audio processing, and/or multi-modal AI systems
Experience with large language models (LLMs) and transformer architectures
Experience in model evaluation, validation, and performance monitoring
Experience with transfer learning and domain adaptation
Programming skills including Python and other relevant languages (C++, Java, Scala, TypeScript)
Version control and collaborative development using Git
Experience with API design and microservices architecture, software testing frameworks, and CI/CD pipelines, plus containerization (Docker, Kubernetes)
Experience with data processing frameworks (Spark, Dask, Ray), feature engineering and data preprocessing
Experience with production model deployment and serving infrastructure, monitoring/logging/observability tools
Experience with cloud platforms (AWS, Azure, GCP) and/or HPC systems, distributed computing/parallel processing, GPU optimization and resource management
Experience with cross-function collaboration and communication, plus technical documentation and presentation
Ability to translate mission requirements into technical solutions

Eligibility and application

Open to: the public
Also open to: U.S. Citizens, Nationals or those who owe allegiance to the U.S.
How to apply: https://apply.intelligencecareers.gov/job-description/1262729

NSA provides a comprehensive benefits package. Agency contact: 1-844-424-4737 or intelcareers@nsa.gov.

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Today

AI Engineer - FDE Software Engineering Sr. Manager

Accenture Overland Park, KS

Overland Park, KS

Today

$123k - $302k/yr Full time Manager Agentic Ai Agentic Ai Orchestration

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Job Description

Accenture is hiring an AI Engineer to embed with enterprise clients as a technologist and trusted advisor. The role focuses on defining, prototyping, and deploying secure, operational agentic workflows, including architecture, AI platform integration, evaluation, and knowledge sharing.

Key Responsibilities

Design and engineer enterprise-ready AI agents, including retrieval, orchestration, policy-based routing, tool invocation, evaluation harnesses, and lifecycle observability.
Develop abstraction layers across AI providers such as Anthropic, Google, and OpenAI to support seamless integration and enablement.
Build scalable AI-native systems using containerization (Kubernetes, Docker), microservices, serverless, event-driven architectures, CI/CD, and observability.
Tailor and deploy agentic applications across verticals such as finance, healthcare, and retail, addressing domain-specific processes through intelligent automation.
Run design workshops, POCs, and code-with sessions to shape data-driven agent workflows with stakeholders and support trust and adoption.
Define and apply key metrics, test harnesses, and evaluation plans to measure agent accuracy, latency, safety, and cost effectiveness.
Create reusable patterns, documentation, and best practices that influence internal assets and client roadmaps.

Required Qualifications

Minimum 10 years of experience in end-to-end software engineering with SDLC expertise.
Minimum 8 years experience in:
- Programming in Python, Java, or equivalent, plus familiarity with evaluation tooling, logging, monitoring, and agent observability.
- Deploying to production, including CI/CD, infrastructure as code (Terraform, Helm), monitoring, and debugging.
- Client communication and collaboration, including leading technical workshops and delivering under ambiguity.
Minimum 3 years experience in:
- Cloud-native systems (APIs, microservices, containerization, serverless).
- AI platforms including OpenAI, Claude, and open-source models, including building abstraction layers for multi-provider pipelines.
Minimum 2 years hands-on experience designing and delivering Agentic AI solutions.
Minimum 1.5 years experience with semantic models, ontologies, knowledge graphs, or enterprise knowledge, and experience using coding agents and AI-assisted software development.
Minimum 1 year expertise designing and deploying agentic solutions (agents, orchestration, context engineering, RAG, workflows) in production environments.
Bachelor’s degree or equivalent (minimum 12 years work experience). If an Associate Degree is held, a minimum of 6 years work experience is required.

Technologies

Python, Java, Anthropic, Google, OpenAI, Claude
Kubernetes, Docker, microservices, serverless
CI/CD, Terraform, Helm
Open-source models, semantic models, ontologies, knowledge graphs, RAG
APIs, event-driven architectures

Role Expectations

Acts as an AI Native Engineer with a minimum of 3 years of experience building cloud-native solutions and deep expertise in designing and deploying agentic systems for enterprise environments.
Applies critical thinking in ambiguous situations to deliver concrete outcomes by designing, building, and running custom AI agents that augment workflows.
Supports the development of a playbook for enterprises adopting and scaling AI-native engineering globally.

The Work

Embeds directly with clients as both a technologist and trusted advisor.
Partners with stakeholders to define use cases, rapidly prototype, and deploy agentic workflows that are robust, secure, and operational within complex enterprise domains.
Integrates agentic capabilities into new or existing platforms and systems, including stitching solutions together in clients’ environments alongside ecosystem partners.

Bonus Points (If Applicable)

Experience as an Agentic AI Engineer in an Enterprise environment.
Additional AI certifications or agentic tool experience.
Experience defining or working with enterprise-grade architectures for compound AI systems, orchestration frameworks, or agent registry/stream-based architectures.
Understanding of the AI-native paradigm combining cloud-native and generative model architectures, focused on performance, modularity, and efficiency.
Delivered solutions across multiple industries, including tailoring agentic workflows for industry needs (for example, finance and healthcare).