Automation & AI Engineer Associate

SAIC is modernizing legacy workloads into secure, cloud-native applications that embed AI and automation for a regulated federal financial environment. This onsite role in Washington, DC focuses on building production LLM and agentic solutions, integrating IRS-related data sources, and applying MLOps/AIOps and responsible AI controls to support governance and security requirements.

Salary: USD 60,001 - 100,000 per year.

What you will do

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

Required qualifications

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (or 4 years of 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.
• Familiarity 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 such as LangChain and LangGraph.
• Proficiency in Python and common ML/NLP libraries (e.g., Hugging Face, Transformers, scikit-learn, PyTorch, TensorFlow).
• Production experience with AWS services including networking/IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, and CloudWatch.
• Practical experience using AWS Bedrock for LLM-powered applications and agents, including knowledge bases and guardrails.
• Experience implementing RAG and working with vector search and vector 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 such as logging, metrics, and drift/quality monitoring for LLM and agent workflows.
• Strong SQL skills and experience integrating legacy data into modern platforms.
• Experience with Docker and container orchestration (Kubernetes, AWS ECS/EKS).

Technologies you may work with

• Python, Hugging Face, Transformers, scikit-learn, PyTorch, TensorFlow
• AWS (Lambda, ECS, EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, CloudWatch) and AWS Bedrock
• LangChain, LangGraph, RAG, vector search
• Terraform, CloudFormation, MLflow, Docker, Kubernetes
• REST APIs, microservices, event-driven systems, CI/CD, MLOps, AIOps
• LangSmith, CrewAI, AutoGen, Temporal, Model Context Protocol (MCP), BrainTrust, DeepEval, Databricks, Delta Lake

Desired experience

• Strong technical judgment and communication skills 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 (for example, LangSmith or similar).
• Experience with advanced agent frameworks such as CrewAI and AutoGen, including multi-agent workflows.
• Hands-on experience operating agents in production, including safety/guardrails, performance tuning, and lifecycle management.
• Experience with durable workflow engines such as Temporal for long-running AI and automation workflows.
• Familiarity with Model Context Protocol (MCP) for tool integration and extensible agent systems.
• Experience with LLM/agent evaluation frameworks such as BrainTrust or DeepEval, or similar.