Automation & AI Engineer Associate

SAIC; Richmond (VA); On-site; USD 120,000 - 180,000; Full time

Job summary

SAIC is seeking an Automation & AI Engineer to modernize legacy systems into intelligent, secure, cloud-native applications for a federal environment. You will collaborate with AI engineers, developers and testers to design scalable, secure AI-driven modernization solutions and integrate legacy data sources into modern platforms.

The role emphasizes hands-on work with LLM frameworks, AWS Bedrock, and MLOps practices to ensure robust security, governance, and observability within a high-value

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (4 years experience in lieu of degree).
• Ability to obtain and maintain a public trust requiring U.S. Citizenship or Green Card.
• 0+ years in software, ML, or data engineering, including experience with application modernization.
• Familiar with AI/ML or LLM-based applications in production.
• Strong experience with modern application architectures (microservices, REST APIs, event-driven) and legacy integration.
• Hands-on experience building agentic AI solutions using LLM frameworks (e.g., LangChain, LangGraph).
• Proficiency in Python and common ML/NLP libraries (e.g., Hugging Face, Transformers, scikit-learn, PyTorch/TensorFlow).
• Production experience with AWS (networking/IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, CloudWatch).
• Practical experience using AWS Bedrock for LLM-powered applications and agents (knowledge bases, guardrails).
• Experience implementing RAG and working with vector search/databases.
• Experience with CI/CD and infrastructure-as-code (e.g., Terraform, CloudFormation).
• Familiarity with MLOps/AIOps (e.g., MLflow, SageMaker) and AI-focused observability (logging, metrics, drift/quality monitoring) for LLM/agent workflows.
• Strong SQL skills and experience integrating legacy data into modern platforms.
• Experience with Docker and container orchestration (Kubernetes, AWS ECS/EKS).

Responsibilities

• Modernize legacy workloads by refactoring monoliths into secure, cloud-native architectures (microservices, APIs, event-driven) with embedded AI/automation.
• Design, build, and deploy LLM- and agentic AI–based solutions that automate complex workflows and integrate with data sources.
• Implement platform engineering and MLOps/AIOps best practices, including CI/CD, infrastructure-as-code, model/prompt lifecycle management, and responsible AI controls.
• Collaborate with architects, developers, testers, and stakeholders to design scalable, secure AI-driven modernization solutions.
• Integrate legacy data sources into modern data platforms and AI-enabled services.
• Ensure compliance with security, privacy, and governance in a regulated federal financial environment.

Skills

Python

LLM frameworks

AWS

Kubernetes

CI/CD

MLOps

Data integration

SQL

REST APIs

Education

Bachelor’s degree in Computer Science or related field

Tools

LangChain

LangGraph

Terraform

CloudFormation

Docker

SageMaker

Job description

SAIC is seeking a self-motivated, customer-focused

Automation & AI Engineer

to join our team. You will work with AI Engineers, Developers, and Testers to modernize legacy systems into intelligent, secure, cloud-native applications. You will help design and enhance a highly secure system for a federal agency that processes high-value financial transactions over the Internet. Experience with payment systems, trading systems, or other highly secure transactional environments is a strong plus. This role is ideal for someone who enjoys solving complex problems with modern AI and cloud technologies in a collaborative team environment.

Key Responsibilities

• Modernize GMF and related legacy workloads by refactoring monoliths and batch processes into secure, cloud-native architectures (microservices, APIs, event-driven systems) with embedded AI/automation.
• Design, build, and deploy LLM- and agentic AI–based solutions (e.g., LangChain, LangGraph, RAG, vector search, AWS Bedrock agents) that automate complex workflows and integrate with IRS data sources.
• Implement platform engineering and MLOps/AIOps best practices, including CI/CD, infrastructure-as-code, model/prompt lifecycle management, and responsible AI controls.
• Collaborate with architects, developers, testers, and stakeholders to design scalable, secure AI-driven modernization solutions.
• Integrate legacy data sources into modern data platforms and AI-enabled services.
• Ensure compliance with security, privacy, and governance requirements in a regulated federal financial environment.

Qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field.(4 years experience in lieu of degree)
• Ability to obtain and maintain a public trust requiring U.S. Citizenship or Green Card.
• 0+ years in software, ML, or data engineering, including experience with application modernization.
• Familiar with AI/ML or LLM-based applications in production.
• Strong experience with modern application architectures (microservices, REST APIs, event-driven) and legacy integration.
• Hands‑on experience building agentic AI solutions using LLM frameworks (e.g., LangChain, LangGraph).
• Proficiency in Python and common ML/NLP libraries (e.g., Hugging Face, Transformers, scikit‑learn, PyTorch/TensorFlow).
• Production experience with AWS (networking/IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, CloudWatch).
• Practical experience using AWS Bedrock for LLM-powered applications and agents (knowledge bases, guardrails).
• Experience implementing RAG and working with vector search/databases.
• Experience with CI/CD and infrastructure-as-code (e.g., Terraform, CloudFormation).
• Familiarity with MLOps/AIOps (e.g., MLflow, SageMaker) and AI-focused observability (logging, metrics, drift/quality monitoring) for LLM/agent workflows.
• Strong SQL skills and experience integrating legacy data into modern platforms.
• Experience with Docker and container orchestration (Kubernetes, AWS ECS/EKS).

Desired

• Strong technical judgment and communication skills; able to explain AI modernization approaches to technical and business stakeholders.
• Experience with Databricks (notebooks, Delta Lake, ML/feature store) for data and ML pipelines.
• Experience with LLM/agent observability and debugging tools (e.g., LangSmith or similar).
• Experience with advanced agent frameworks (e.g., CrewAI, AutoGen) and multi-agent workflows.
• Hands‑on experience operating agents in production (safety/guardrails, performance tuning, lifecycle management).
• Experience with durable workflow engines (e.g., Temporal) for long-running AI/automation workflows.
• Familiarity with Model Context Protocol (MCP) for tool integration and extensible agent systems.
• Experience with LLM/agent evaluation frameworks (e.g., BrainTrust, DeepEval, or similar).

SAIC is a premier mission integrator focused on advancing the power of technology and innovation to serve and protect our world. Our robust portfolio of offerings across the defense, space, intelligence, and civilian markets includes secure high-end solutions in mission IT, enterprise IT, engineering services, and professional services. We integrate emerging technology, rapidly and securely, into mission critical operations that modernize and enable critical national imperatives.
We are approximately 23,000 strong; driven by mission, united by purpose, and inspired by opportunities. SAIC is an Equal Opportunity Employer. Headquartered in Reston, Virginia, SAIC has annual revenues of approximately $7.3 billion. For more information, visit saic.com. For ongoing news, please visit our newsroom.