SAIC is hiring an Automation & AI Engineer Associate to help modernize legacy GMF workloads into secure, cloud-native applications with embedded AI and automation. The role focuses on building LLM and agentic solutions, integrating legacy and IRS data sources, and applying MLOps/AIOps best practices along with responsible AI controls in a regulated federal financial environment.
Location: Washington, DC, US (onsite).
Compensation: USD 60,001 - 100,000 per year.
What you’ll 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- and agentic-based solutions that automate complex workflows and integrate with IRS data sources (examples include LangChain, LangGraph, RAG, vector search, and AWS Bedrock agents).
- Implement platform engineering and MLOps/AIOps practices such as CI/CD, infrastructure-as-code, and model or prompt lifecycle management, including responsible AI controls.
- Partner with architects, developers, testers, and stakeholders to deliver scalable, secure modernization solutions.
- Integrate legacy data sources into modern data platforms and AI-enabled services.
- Support security, privacy, and governance requirements required for a regulated federal financial environment.
What you bring
- 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 of software, ML, or data engineering experience, including 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, including Hugging Face, Transformers, scikit-learn, and 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 (examples: Terraform, CloudFormation).
- Familiarity with MLOps/AIOps concepts (examples: MLflow, SageMaker) and AI-focused observability such as logging, metrics, and 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).
Technologies you may use
Python, Hugging Face, Transformers, scikit-learn, PyTorch, TensorFlow, AWS (Lambda, ECS, EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, CloudWatch, AWS Bedrock), LangChain, LangGraph, RAG, vector search, Terraform, CloudFormation, MLflow, Docker, Kubernetes, REST APIs, microservices, event-driven, CI/CD, MLOps, AIOps, LangSmith, CrewAI, AutoGen, Temporal, Model Context Protocol (MCP), BrainTrust, DeepEval, Databricks, Delta Lake.
Desired background
- Strong technical judgment and communication skills, including the ability to explain AI modernization approaches to technical and business stakeholders.
- Experience with Databricks for data and ML pipelines (including notebooks, Delta Lake, and ML/feature store).
- Experience with LLM/agent observability and debugging tools (for example, LangSmith or similar).
- Experience with advanced agent frameworks and multi-agent workflows (for example, CrewAI or AutoGen).
- Hands-on experience operating agents in production with safety/guardrails, performance tuning, and lifecycle management.
- Experience with durable workflow engines for long-running AI and automation workflows (for example, Temporal).
- Familiarity with Model Context Protocol (MCP) for tool integration and extensible agent systems.
- Experience with LLM/agent evaluation frameworks (for example, BrainTrust, DeepEval, or similar).
