Note: The job is a remote job and is open to candidates in USA. SAIC is a mission integrator providing secure technology and innovation solutions across defense, intelligence, civilian, and enterprise IT markets. The Automation & AI Engineer will modernize legacy federal financial systems into secure, cloud-native applications by designing AI- and automation-enabled solutions, implementing MLOps and platform engineering practices, integrating legacy data, and ensuring security and governance compliance.
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
Skills
- 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)
- Experience with payment systems, trading systems, or other highly secure transactional environments
- 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)
Benefits
- Remote work arrangement
Company Overview