Architecture

Architecture Data Jobs

3 jobs found

Today

Principal AI Engineer (SDLC)

AT&T Dallas, TX

Dallas, TX

Today

Full time Backend Developer Agentic Ai Ai Agent

Apply Now

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

View full job page →

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

Apply Now

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

View full job page →

1mo ago

AI Engineer

Guidehouse Inc. McLean, VA

McLean, VA

1mo ago Closed

$98k - $163k/yr Full time Agentic Ai Ai Engineering Architecture

This position is no longer accepting applications (closed Sep 3, 2026).

Job Description

Guidehouse Inc. is seeking an AI Engineer in McLean, VA (onsite) to build, test, and deploy AI applications and services. The work spans data and ML pipelines, retrieval-augmented generation, agentic workflows, and the integration of LLM capabilities into production-ready systems.

Key Responsibilities

Build, test, and deploy AI applications and services by translating solution designs and reference architectures into demo-ready components.
Develop data and ML pipelines, including ingest, transform, feature stores, and vector indexes, and connect them to RAG and agentic workflows.
Package and serve models (LLMs and traditional ML) through APIs and microservices using containers and orchestration tooling such as Docker and Kubernetes.
Stand up and maintain cloud and AI platform resources across environments including AWS, Azure, GCP, Palantir, and Databricks, with support for CI/CD, infrastructure as code (for example, Terraform), secrets management, and observability.
Integrate AI capabilities into applications and services, including prompt orchestration, tool/function calling, embeddings, and fine-tuning.
Partner with data scientists, platform engineers, and product teams to iterate on use cases, deliver POCs/MVPs, and harden solutions for scale.
Support demos, technical documentation, and solution content for proposals and pitch materials.
Apply responsible AI practices and follow security and compliance requirements across both commercial and public sector environments.

Required Qualifications

US Citizenship is required.
Bachelor’s degree is required.
Minimum 3 years of experience in software, data, or ML engineering, including building and operating cloud-native services.
Minimum 1 year of hands-on experience with Generative AI and/or agentic patterns such as RAG, function/tool calling, and prompt orchestration.
Proficiency with at least one major cloud (AWS, Azure, or GCP) plus modern DevOps practices including Git, CI/CD, containerization, and infrastructure as code.
Familiarity with vector databases and embeddings, along with LLM application frameworks.
Ability to troubleshoot production systems using logs, metrics, and traces; write clear documentation and runbooks; and collaborate effectively across teams.
Growth mindset with interest in expanding responsibilities into broader architecture over time.

Technologies

AWS, Azure, GCP, Palantir, Databricks
Docker, Kubernetes
Terraform, CI/CD, IaC, Git
Vector databases, embeddings
LLM application frameworks
Retrieval-augmented generation (RAG), prompt orchestration, tool/function calling, fine-tuning
Microservices, APIs

Benefits

Medical, Rx, Dental & Vision Insurance
Personal and Family Sick Time & Company Paid Holidays
Position may be eligible for a discretionary variable incentive bonus
Parental Leave and Adoption Assistance
401(k) Retirement Plan
Basic Life & Supplemental Life
Health Savings Account, Dental/Vision & Dependent Care Flexible Spending Accounts
Short-Term & Long-Term Disability
Student Loan PayDown
Tuition Reimbursement, Personal Development & Learning Opportunities
Skills Development & Certifications
Employee Referral Program
Corporate Sponsored Events & Community Outreach
Emergency Back-Up Childcare Program
Mobility Stipend

Additional Information

Salary: USD 98,000 - 163,000 per yearly
Location: McLean, VA (onsite)
Travel Required: Up to 10%
Clearance Required: None
JOB FAMILY: Engineering Consulting
WHAT WOULD BE NICE TO HAVE: Ability to obtain and maintain a Federal or DoD SECRET security clearance (active clearance preferred); certifications in cloud architecture, DevOps, or AI/ML (for example AWS/Azure/GCP, Databricks, Kubernetes); experience contributing to client-facing engineering in consulting or product environments; Master’s degree
WHAT WE OFFER: A comprehensive, total rewards package including competitive compensation and a flexible benefits package supporting a diverse and supportive workplace

View full job page →

Architecture Data Jobs

Principal AI Engineer (SDLC)

AI Engineer - FDE Software Engineering Sr. Manager

AI Engineer

Get Architecture Data Jobs alerts