Job DescriptionSAIC 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.
QualificationsRequired - Bachelor's degree in Computer Science, Engineering, Data Science, or related field.
- Ability to obtain and maintain a public trust requiring U.S. Citizenship or Green Card.
- 9+ years in software, ML, or data engineering, including experience with application modernization.
- 4+ years building and deploying 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).
