Automation & AI Engineer
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Job Description
Automation & AI Engineer modernizing secure federal financial transaction systems for SAIC. Building production LLM, agentic AI, and cloud-native solutions.
Responsibilities:
- Modernize GMF and related legacy workloads by refactoring monoliths and batch processes into secure, cloud-native architectures with embedded AI/automation
- Design, build, and deploy LLM- and agentic AI-based solutions using technologies such as LangChain, LangGraph, RAG, vector search, and AWS Bedrock agents
- Automate complex workflows and integrate solutions 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
Requirements:
- 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 such as LangChain and LangGraph
- Proficiency in Python and common ML/NLP libraries such as Hugging Face, Transformers, scikit-learn, PyTorch/TensorFlow
- Production experience with AWS, 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/databases
- Experience with CI/CD and infrastructure-as-code such as Terraform or CloudFormation
- Familiarity with MLOps/AIOps such as MLflow or SageMaker and AI-focused observability
- Strong SQL skills and experience integrating legacy data into modern platforms
- Experience with Docker and container orchestration such as Kubernetes or AWS ECS/EKS
- Desired: Experience with Databricks, LangSmith or similar tools, CrewAI, AutoGen, Temporal, Model Context Protocol, BrainTrust, DeepEval, or similar frameworks
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