Rag Architectures

Rag Architectures Data Jobs

60 jobs found

10d ago

AI Engineer - Senior

NENI TECHSYSTEMS INC. Plano, TX

Plano, TX

10d ago

$55 - $75/hr Full time Backend Developer Agentic Ai Ai Agent

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Job Description

NENI TECHSYSTEMS INC. is hiring a Senior AI Engineer (Agentic Systems) to design, build, and deploy production-grade large language model and agentic AI solutions. This is an onsite opportunity based in Plano, TX (also listed for Columbus, OH, New York, NY, and Jersey City, NJ), with a focus on delivering LLM-based agent systems that operate reliably in production environments.

The role emphasizes hands-on engineering across the full lifecycle of agentic systems, from orchestration and retrieval to evaluation, testing, and operational readiness, with a preference for candidates familiar with regulated domains such as Banking/Financial Services.

Responsibilities

Build and deploy production-grade LLM and agentic AI systems.
Deliver LLM-based agentic systems in production.

Requirements

7+ years of software engineering experience, with senior or lead-level ownership.
Proven experience delivering LLM-based agentic systems in production.
Strong hands-on experience with Python and Java/Spring.
Expertise with LangChain, LangGraph, or equivalent agent orchestration frameworks.
Experience with RAG, tool/function calling, multi-turn conversations, escalation, and human handoff.
Strong understanding of prompt/context engineering and agent state management.
Experience with LLM evaluation, regression testing, trace analysis, and A/B testing.
Hands-on experience with vLLM or comparable LLM inference/serving platforms and commercial LLM APIs.
Working knowledge of PyTorch and LLM/model fundamentals.
Strong distributed systems experience including APIs, queues, caching, observability, and CI/CD.
Experience implementing guardrails, PII handling, auditability, deterministic fallbacks, and production reliability.
Local to the selected location (within approximately 30–40 miles) and able to work onsite; no relocation candidates.
Available for an onsite interview.

Preferred

Experience with agent-building platforms such as Sierra or Decagon.
Voice agents, streaming inference, or contact-center integrations.
Experience with banking, financial services, healthcare, or other regulated-industry environments.
Experience with LangSmith, Braintrust, or custom LLM evaluation frameworks.
Knowledge of LLM safety and guardrail technologies.

Technologies

Python, Java, Spring
LangChain, LangGraph
RAG, vLLM, PyTorch
CI/CD

Work Arrangement

Onsite (in person) with an onsite interview requirement.
Locations: Plano, TX / Columbus, OH / New York, NY / Jersey City, NJ.

Pay and Hours

$55.00 - $75.00 per hour
40.0 hours per week

Visa

USC, EAD, H1B

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11d ago

Machine Learning Engineer II

S&P Global New York, NY

New York, NY

11d ago

$140k - $180k/yr Full time Agentic Ai Agentic Ai Orchestration Ai Ml

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Job Description

Build agentic AI systems for financial data at S&P Global in New York, NY (onsite), spanning LLM orchestration, retrieval, evaluation, and the full ML lifecycle.

Responsibilities

Address distinctive challenges in agentic design and LLM orchestration, including context engineering, data access patterns, memory management, and agent performance evaluation
Contribute across the ML lifecycle, from problem framing and data exploration to experimentation, deployment, and monitoring in production for continuous improvement of Agentic Systems
Apply advanced NLP techniques using proprietary unstructured and structured datasets to extract insights and create solutions tied to business value
Partner with Data, Product, Design, and Engineering teams to design and develop Agents that improve user experience and support business objectives
Collaborate with ML Operations to automate the full ML systems lifecycle, from initial technical design through implementation

Requirements

Bachelor’s degree (or higher) in Computer Science, Engineering, or a related field
3+ years of significant, hands-on industry experience in machine learning, NLP, and information retrieval systems, focused on practical applications
Experience covering all phases of the ML life-cycle, including designing, experimenting, deploying, and maintaining production systems
Strong Python skills and familiarity with software development best practices
Experience using machine learning libraries and frameworks for agent orchestration, including LangGraph and pydanticAI (and similar tools)
Knowledge of agentic design, including user interaction understanding and agent performance evaluation to enhance user experiences
Strong habits in coding, documentation, collaboration, and communication
Proven problem-solving ability and a proactive approach to tackling challenges
Ability to adapt in a fast-paced and dynamic work environment

Technologies

Agentic Orchestration
Deep Research
Information Retrieval
Semantic Search
LLM code generation
LLM tool utilization
Textual RAG systems
LangGraph
Transformers
HuggingFace
LightGBM
PyTorch
SKLearn
XGBoost
Jupyter
Matplotlib
Pandas
Weights & Biases
Langfuse
Apache Spark
AWS Athena
DVC
LabelBox
OpenSearch
Postgres/Pgvector
S3
SQLite
Arize
Airflow
AWS
DeepSpeed
Docker
Grafana
Jenkins
LangFuse
LiteLLM
Ray
vLLM
Claude Code
FastAPI
Streamlit
Gradio
Python
pydanticAI

Benefits

Medical, Dental, and Vision insurance
100% company paid premiums
Unlimited Paid Time Off
26 weeks of 100% paid Parental Leave (paternity and maternity)
401(k) plan with 6% employer matching
Generous company matching on donations to non-profit charities
Up to $20,000 tuition assistance toward degree programs, plus up to $4,000/year for ongoing professional education such as industry conferences
Plentiful snacks, drinks, and regularly catered lunches
Dog-friendly office (CAM office)
Bike sharing program memberships
Compassion leave and elder care leave
Mentoring and additional learning opportunities
Opportunity to expand professional network and participate in conferences and events

Recruitment Fraud Alert

If you receive an email from a spglobalind.com domain or any other regionally based domains, it is a scam and should be reported to reportfraud@spglobal.com
S&P Global never requires any candidate to pay money for job applications, interviews, offer letters, “pre-employment training” or for equipment/delivery of equipment

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11d ago

AI Engineer

SCIGON Chicago, IL

Chicago, IL

11d ago

$52 - $70/hr Full time AI Artificial Intelligence Azure Ai

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Job Description

SCIGON is seeking an Applied AI Engineer to design, deploy, and operationalize enterprise AI capabilities. In this onsite role in Chicago, IL, you will help implement LLM-powered solutions with enterprise governance, security, and integration into existing systems, turning AI enablement into reliable production capabilities for business teams.

What you’ll work on

Design, deploy, and manage reusable AI capabilities within enterprise AI platforms, including skills, tools, and plug-in functionality.
Own the end-to-end lifecycle of AI enablement solutions, from intake and configuration through deployment, governance, ongoing support, and optimization.
Configure and apply foundation models such as Claude, Gemini, and similar technologies to support enterprise automation and business workflows.
Deliver solutions from concept to production implementation with scalability, security, and operational readiness in mind.
Integrate enterprise systems, services, and approved tools using available connectivity frameworks and protocols.
Collaborate with Security, Cloud, and Infrastructure teams to implement access controls, identity management practices, and credential governance.
Ensure compliance with AI governance standards, including auditability, monitoring, risk controls, and operational guardrails.
Work across engineering, automation, data, and business teams to identify opportunities and deliver AI-driven capabilities that create measurable impact.

Key requirements

Proven experience developing, publishing, and managing Claude Skills or plug-ins in a production environment.
Demonstrated success deploying AI capabilities that are actively used by business stakeholders.
Ability to contribute quickly with minimal onboarding and ramp-up time.
Experience building and deploying solutions in regulated or highly governed enterprise environments.
Strong understanding of security controls, compliance requirements, access management, and operational governance.
Comfort working within structured delivery processes and change-control frameworks.
Strong communication and stakeholder management skills across technical and non-technical audiences.
Ability to translate business challenges into practical AI-enabled solutions.
5+ years in software engineering, automation engineering, or a related technical discipline.
At least 2 years designing and deploying production-grade LLM-powered solutions.
Strong proficiency in Python and API-based integrations.
Experience with enterprise integration patterns and distributed systems.
Solid engineering practices including testing, source control, observability, monitoring, and supportability.
Hands-on experience with modern LLM ecosystems, including prompt engineering, model configuration, tool integration, and function execution.
Experience working in public cloud environments such as Azure, Google Cloud Platform, Vertex AI, or equivalents.
Ability to evaluate AI use cases pragmatically and determine when traditional engineering approaches may be more effective.
Self-directed, able to independently lead technical initiatives in a fast-moving environment.

Technologies you may use

Claude, Gemini, Python, Azure, Google Cloud Platform, Vertex AI, LLM ecosystems, Model Context Protocol (MCP), RAG architectures, vector databases, embeddings, semantic search solutions, RBAC

Preferred qualifications

Experience building AI agents and multi-agent workflows.
Familiarity with orchestration platforms and agent frameworks.
Understanding of Model Context Protocol (MCP) implementations and agent-to-agent integrations.
Experience with RAG architectures, vector databases, embeddings, and semantic search solutions.
Knowledge of modern identity and access management concepts, including RBAC, service accounts, agent identities, and least-privilege models.
Previous experience supporting organizations in highly regulated industries such as insurance, financial services, healthcare, or similar sectors.

Compensation: USD 52 - 70 per hour.

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11d ago

Senior AI Engineer – Agentic AI Platform

Hudson Manpower Cincinnati, OH

Cincinnati, OH

11d ago

$55 - $60/hr Full time Backend Developer Agentic Ai Ai Agent

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Job Description

Hudson Manpower is hiring a Senior AI Engineer to help design and deliver an enterprise-scale agentic AI platform. This onsite role in Cincinnati, OH focuses on building the capabilities needed to create, deploy, monitor, govern, and operate autonomous AI agents across business domains, with a cloud-native approach on Azure.

The work spans multi-agent orchestration patterns, AI platform architecture, governance and FinOps controls, memory and RAG, observability and evaluation, and responsible AI guardrails for sensitive, regulated enterprise contexts.

What you will do

Design and develop multi-agent AI systems for enterprise use cases.
Build autonomous and semi-autonomous AI workflows.
Implement agent orchestration and workflow patterns including Supervisor-Worker, Sequential, ReAct, Planner-Executor, and Writer-Critic, including orchestration and choreography approaches.
Develop scalable frameworks for agent communication and execution.
Create closed-loop workflows that include validation, retry, evaluation, and feedback mechanisms.
Build reusable AI platform capabilities that multiple business teams can use.
Design enterprise AI governance and operational controls for agent operations.
Develop API-driven AI services with operational and security features such as rate limiting, quota management, authentication, authorization, audit logging, multi-tenant usage tracking, and cost attribution.
Establish agent onboarding and lifecycle management capabilities.
Implement agent communication using direct calls, event-driven architectures, message queues, and publish-subscribe patterns.
Work with Kafka and Azure event tooling including Azure Service Bus and Azure Durable Functions to support event-driven workflows.
Design short-term and long-term AI memory architectures, including vector databases, semantic caching, conversation memory, and agent state persistence.
Build knowledge orchestration frameworks for agent collaboration, including support for graph databases and enterprise knowledge models.
Support ontology-driven AI applications and implement knowledge graphs for relationship-based reasoning.
Integrate structured, unstructured, and graph-based knowledge sources.
Implement AI consumption governance across business domains, including token usage tracking and operational cost monitoring.
Develop chargeback/showback mechanisms and support AI FinOps reporting and capacity planning.
Design observability for AI applications, including monitoring agent execution, tool usage, latency, hallucinations, failure rates, and model quality.
Build dashboards and operational metrics for AI workloads.
Implement guardrails and safety controls including prompt protection, data masking, PII protection, and human-in-the-loop validation.
Ensure compliance with enterprise security and governance requirements, including secure agentic AI systems for sensitive business data.
Develop agent and tool evaluation frameworks, including response quality measurement and hallucination detection, and implement closed-loop evaluation mechanisms.
Apply context engineering, prompt engineering, retrieval optimization, agent tuning, and AI benchmarking.

Minimum requirements

8–10 years of software engineering or platform engineering experience.
3+ years of hands-on AI/ML or Generative AI experience.
Production experience building enterprise-scale AI applications.
Strong experience designing AI architectures and platforms (not only individual AI applications).
Hands-on experience with Agentic AI / AI Agents.
Strong experience with Azure, including Azure AI Foundry and Azure OpenAI.
Strong experience with LangChain and/or LangGraph.
Strong Python development experience.
Experience with multi-agent orchestration and agentic workflow patterns.
Experience with RAG, vector databases, AI memory, and agent state management.
Experience with REST APIs and API gateways, preferably Azure API Management (APIM).
Experience with event-driven architectures and messaging systems.
Experience with AI monitoring, observability, governance, and cost/token usage tracking.
Experience with enterprise data/storage technologies such as Cosmos DB, PostgreSQL, MongoDB, or vector databases.
Experience with SQL.
Experience designing scalable, secure, and governed AI platforms.

Useful technologies

Azure, Azure AI Foundry, Azure OpenAI, LangChain, LangGraph, Python
Vector databases, RAG, REST APIs, API gateways, Azure API Management (APIM)
Kafka, Azure Service Bus, Azure Durable Functions
Message queues, publish-subscribe patterns, event-driven architectures
Cosmos DB, PostgreSQL, MongoDB, SQL

Desirable skills

Semantic Kernel
Model Context Protocol (MCP)
C# / .NET
Azure Event Grid
Graph databases such as Neo4j, Stardog, Amazon Neptune, or other graph databases
Enterprise knowledge graphs and ontology-driven AI solutions
AI FinOps and chargeback/showback, responsible AI frameworks, and AI evaluation and benchmarking
AWS or GCP experience
Experience in healthcare, financial services, insurance, or other regulated industries

Location: Cincinnati, OH (onsite). Compensation: USD 55–60 per hour.

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11d ago

AI Engineer

Suffolk Construction Company, Inc. Boston, MA

Boston, MA

11d ago

$107k - $223k/yr Full time Backend Developer Artificial Intelligence AWS

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Job Description

Build production-grade AI systems for construction workflows, translating requirements into end-to-end solutions on AWS and Databricks.

Responsibilities

Translate product requirements and user stories into production-grade AI solutions using AWS Bedrock, AWS Lambda, ECS/EKS, and Databricks
Implement RAG pipelines with Delta tables, Unity Catalog, and Vector Search
Design and deploy multi-model agents that dynamically route between Claude, GPT, Llama, Titan (based on task context, cost, and latency)
Implement multi-agent orchestration frameworks for collaboration among specialized agents (for example: data retriever, planner, summarizer, action executor) for complex construction workflows
Own the full lifecycle: design, development, testing, deployment, monitoring, and maintenance
Build APIs, backend services, and agentic workflows using Python, FastAPI, LangChain, and AWS SDKs
Create reusable connectors and orchestration layers for multi-model agents (Claude, GPT, Llama, etc.)
Develop front-end integrations for Teams and web SPAs using REST or GraphQL endpoints
Partner with Data Engineering to design robust ETL/ELT pipelines from enterprise systems to the Databricks Lakehouse
Ensure efficient data access, caching, and vectorization for low-latency AI responses
Build tools to monitor and improve data quality, latency, and observability
Automate infrastructure and deployments with Terraform, AWS CDK, and GitHub Actions
Implement LLMOps practices including cost monitoring, latency optimization, usage analytics, and model versioning
Enforce security, governance, and access standards aligned with enterprise policies
Collaborate with product managers, site AI engineers, and data scientists to iterate quickly in Agile sprints
Communicate technical progress to non-technical stakeholders and contribute to internal AI playbooks and templates

Requirements

4-6 years of professional software development experience on AWS, with 2+ years focused on AI/ML engineering (LLMs, RAG, Bedrock, or similar)
Strong coding proficiency in Python (LangChain, FastAPI, boto3) and solid experience with SQL, Databricks, and vector databases
Experience designing and deploying production systems using AWS Lambda, ECS/EKS, API Gateway, Step Functions, S3, CloudFront, and KMS
Strong foundation in CI/CD, IaC (Terraform/CDK), and GitHub Actions
Experience training, retraining, and performing transfer learning on ML models is desirable
Bachelor’s degree in Computer Science, Engineering, Physics, or a related field; Master’s preferred
Hands-on experience in construction or heavy process industries (manufacturing, oil & gas, chemicals) is a significant plus
Excellent collaboration and communication skills; capable of working cross-functionally without reliance on business-side facilitation
Integration and ETL skills: foundational ETL/ELT design, experience with Airflow or Databricks Workflows, and REST/GraphQL API development; proven collaboration with Data Engineering on source-to-lake and lake-to-agent pipelines

Technologies

AWS Bedrock, AWS Lambda, ECS, EKS, Databricks
Delta tables, Unity Catalog, Vector Search
Claude, GPT, Llama, Titan
Python, FastAPI, LangChain, AWS SDKs, boto3
SQL, vector databases
API Gateway, Step Functions, S3, CloudFront, KMS
Terraform, AWS CDK, GitHub Actions, CI/CD
LLMOps, ETL/ELT, Airflow, Databricks Workflows, REST, GraphQL

Benefits

Competitive salaries
Auto allowances and gas cards for certain roles
Market leading medical and emotional and mental health benefits
Dental and vision insurance plans
Virtual care options for physical therapy and primary care
Generous paid time off
401k plan with employer match
Access to expert financial resources
Company paid and voluntary life insurance
Tax deferred savings accounts
10 backup daycare days each year
Short- and long-term disability
Commuter benefits

Working Conditions

Regularly required to sit for long periods of time; talk or hear; perform fine motor, hand and finger skills using a keyboard, telephone, or writing
Frequently required to stand, walk, and reach with arms and/or hands
Specific vision abilities include close vision, distance vision, depth perception, and the ability to adjust focus
Office environment with quiet to moderate noise level
Job site walking

EEO Statement

Suffolk provides equal employment opportunities to all employees and applicants for employment without regard to race, color, religion, sex, sexual orientation, pregnancy or maternity, national origin, citizenship, genetic information, disability, protected veteran, gender identity, age or any other status protected by law
This policy applies to recruiting, hiring, transfers, promotions, terminations, compensation, benefits, and all other terms and conditions of employment
Suffolk will not tolerate any unlawful discrimination toward, or harassment of, applicants or employees by anyone at Suffolk, or anyone working on behalf of Suffolk

Compensation Information

Where required by law, pay ranges can be found in Suffolk's job postings
Base Salary for this position is one component of Suffolk’s total rewards package
Actual salaries may be based on skill set, experience, education, and other qualifications
Suffolk offers a comprehensive benefits package as part of its overall total rewards strategy
Salary ranges are reviewed regularly to reflect market trends

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12d ago

AI Engineer II

Blue Origin Seattle, WA

Seattle, WA

12d ago

$135k - $189k/yr Full time Agentic Ai Ai Agent Ai Agent Platform

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Job Description

AI Engineer II role on Blue Origin’s Blue Ring ground systems team, building AI/ML capabilities for satellite fleet operations, including multi-agent monitoring, conversational RAG knowledge retrieval, and ML-driven anomaly detection.

Responsibilities

Implement and test agent components within a multi-agent architecture for telemetry analysis, anomaly detection, and reporting
Build and maintain integrations connecting agents to ground system services
Create agent tooling, prompt templates, and orchestration logic for LLM service integrations
Build and maintain RAG pipeline components: document ingestion, embedding generation, and vector database indexing
Implement retrieval strategies and answer generation workflows for operator queries
Implement components for human-in-the-loop decision workflows, including investigation packages and remediation options
Deploy and maintain containerized AI services using Docker and AWS infrastructure
Contribute to audit trail, observability, and production operations, including incident response
Prepare and transform spacecraft telemetry data to support ML model training
Assist in training and evaluating anomaly detection and time-series forecasting models on telemetry data
Support automated model retraining pipelines that incorporate new flight data and operator feedback
Develop model evaluation scripts and implement metrics tracking

Requirements

Ability to work onsite in one of: Kent, WA, Renton, WA, or Reston, VA
Bachelor’s degree in Computer Science, Machine Learning, Electrical Engineering, or a related field
3+ years of professional software development experience with exposure to AI/ML applications
Proficiency in Python and familiarity with at least one AI/ML framework: PyTorch or TensorFlow
Experience building or contributing to LLM-based applications, RAG pipelines, or agent-based systems
Exposure to time-series data processing, anomaly detection, or ML model training on sensor or telemetry data
Experience with AWS (preferred), Docker, and basic CI/CD practices
Solid understanding of CS fundamentals: data structures, algorithms, object-oriented design, and version control
Strong analytical and problem-solving skills with attention to detail
Good written and verbal communication skills for documentation and team collaboration

Technologies

Python, PyTorch, TensorFlow
LLM, RAG
Docker, AWS, CI/CD
Vector database

Benefits

Medical, dental, vision
Basic and supplemental life insurance
Paid parental leave
Short- and long-term disability
401(k) with company match up to 5%
Education Support Program
Stock Options for all regular employees working at least 20 hours/week
Paid Time Off: up to four (4) weeks per year based on weekly scheduled hours
Up to 14 company-paid holidays

Preferred Qualifications

Experience with AWS Bedrock, SageMaker, or equivalent managed AI/ML services
Experience with Databricks or similar platforms for data engineering and ML workflows
Familiarity with vector databases (e.g., Pinecone, pgvector, FAISS) and embedding model pipelines
Experience with time-series ML architectures (RNNs, LSTMs, Transformers) for forecasting or anomaly detection
Exposure to multi-agent system frameworks or agentic AI orchestration patterns
Familiarity with spacecraft operations, satellite telemetry, or aerospace engineering concepts
Experience with infrastructure-as-code tools such as Terraform and Kubernetes
Experience with real-time data streaming systems (e.g., Kafka, Kinesis)
Familiarity with reinforcement learning concepts or graph neural network applications
Interest or experience in model compression, edge deployment, or resource-constrained inference

Export Control Regulations

Applicants must be a U.S. citizen or national, U.S. permanent resident (current Green Card holder), or lawfully admitted into the U.S. as a refugee or granted asylum.

Background Check

Required for all positions: Blue’s Standard Background Check
Required for certain job profiles: DBIDS background check if the role requires time on a military installation
Required for certain job profiles: Drivers operating Commercial Motor Vehicles meeting specified weight and/or hazardous materials criteria may be subject to additional Federal Motor Carrier Safety Regulations
Required for certain job profiles: Ability to obtain and maintain a Merchant Mariner Credential, including pre-employment and random drug testing and DOT physical

Affirmative Action and Disability Accommodation

For information on Blue Origin’s Affirmative Action Plans or to request a reasonable accommodation for the application and/or interview process, contact EEOCompliance@blueorigin.com.

Location: Seattle, WA (onsite) · Compensation: USD 134,961 - 188,945 per year · Experience: 3+ years · Education: Bachelor’s degree in a related field

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12d ago

AI Engineer

Capgemini Atlanta, GA

Atlanta, GA

12d ago

$41 - $65/hr Contract Backend Developer Ai Agent Platform Artificial Intelligence

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Job Description

Capgemini is seeking a hands-on AI Engineer in Atlanta, GA to enhance and optimize an existing AI platform built on Claude Sonnet and the Model Context Protocol (MCP). This is a contract role focused on improving AI agents, MCP tools and servers, enterprise integrations, and the overall quality, performance, security, and scalability of the platform.

Role Overview

You will work directly on MCP-based AI applications and agents, expanding core platform capabilities and strengthening end-to-end behavior. The role emphasizes improved context handling, reliable tool execution, and production readiness through monitoring, logging, security controls, and performance enhancements.

Responsibilities

Enhance and maintain MCP-based AI applications and agents
Develop and improve MCP servers, tools, and integrations
Integrate AI solutions with enterprise systems, APIs, databases, and knowledge repositories
Improve prompt engineering, context management, and response accuracy
Build and optimize RAG (Retrieval-Augmented Generation) solutions
Implement monitoring, logging, security controls, and performance improvements
Troubleshoot AI agent behavior, tool execution, and platform issues
Support deployment, testing, and continuous enhancement of AI capabilities

Required Qualifications

3+ years of software development experience using Python and/or TypeScript/Node.js
Hands-on experience building LLM / Generative AI applications
Strong understanding of MCP (Model Context Protocol), AI agents, and tool calling
Experience with Claude, OpenAI, or similar LLM platforms
Experience integrating APIs, databases, and enterprise applications
Knowledge of RAG, vector databases, embeddings, and prompt engineering
Strong debugging, problem-solving, and communication skills

Preferred Skills

Experience with LangChain, LangGraph, Semantic Kernel, or similar frameworks
Experience with AI observability, evaluation frameworks, and prompt optimization
Knowledge of security, authentication, and access control for AI applications
Experience deploying production-grade AI solutions

Technologies

Claude Sonnet, Model Context Protocol (MCP), TypeScript, Node.js, Python, LLM / Generative AI, RAG (Retrieval-Augmented Generation), vector databases, embeddings, LangChain, LangGraph, Semantic Kernel