Automation & AI Engineer

SAIC; Washington (District of Columbia); Hybrid; USD 120,000 - 160,000; Full time

Job summary

SAIC seeks an Automation & AI Engineer in a hybrid role based in Washington, DC to modernize legacy GMF workloads into secure, cloud-native applications for a federal setting.

You will design and deploy LLM/agentic AI solutions, apply MLOps best practices, and ensure governance for high-value financial transactions. Strong AWS, LangChain, LangGraph, and Python experience are required.

Qualifications

• Bachelor's degree in Computer Science, Engineering, Data Science, or a 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 microservices, REST APIs, event-driven architectures and legacy integration.
• Hands-on experience with agentic AI using LangChain and LangGraph.
• Proficiency in Python and ML/NLP libraries (Hugging Face, Transformers, scikit-learn, PyTorch, TensorFlow).
• Production experience with AWS services (Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, CloudWatch).
• Experience with AWS Bedrock, RAG, vector search, and guardrails.

Responsibilities

• Refactor legacy workloads into secure cloud-native architectures with microservices and event-driven systems.
• Design, build, and deploy LLM and agentic AI solutions using LangChain and LangGraph.
• Apply platform engineering and MLOps/AIOps best practices including CI/CD and infrastructure-as-code.
• Collaborate with architects, developers, testers, and stakeholders to deliver scalable modernization.
• Integrate legacy data sources into modern data platforms and AI-enabled services.
• Support security, privacy, and governance requirements for a regulated federal financial environment.

Skills

Advanced AI/ML

Cloud architecture

Security & governance

Communication with stakeholders

Education

Bachelor's degree in CS/Engineering/DS

Tools

LangChain

LangGraph

AWS Bedrock

Terraform

CloudFormation

Kubernetes

Docker

SageMaker

OpenSearch

Job description

SAIC is hiring an Automation & AI Engineer for a hybrid role in Washington, DC. In this position, you will help modernize legacy GMF and related workloads into secure, cloud-native applications for a federal agency environment involving high-value financial transactions. The work blends LLM and agentic AI solutions with platform engineering, MLOps/AIOps, and regulated governance to support modernization at scale.

Compensation: USD 120,001 - 160,000 per year.

Responsibilities

• Refactor monoliths and batch processes for GMF and related legacy workloads into secure cloud-native architectures including microservices, REST APIs, and event-driven systems with embedded AI automation.
• Design, build, and deploy LLM and agentic AI solutions using frameworks such as LangChain and LangGraph, including approaches like RAG and vector search with AWS Bedrock agents to automate complex workflows and integrate with IRS data sources.
• Apply platform engineering and MLOps/AIOps best practices, including CI/CD, infrastructure-as-code, and model or prompt lifecycle management, along with responsible AI controls.
• Partner with architects, developers, testers, and stakeholders to deliver scalable, secure modernization solutions driven by AI.
• Integrate legacy data sources into modern data platforms and AI-enabled services.
• Support security, privacy, and governance requirements for 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 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 ML/NLP libraries including Hugging Face, Transformers, scikit‑learn, PyTorch, and TensorFlow.
• Production experience with AWS services including networking/IAM, Lambda, ECS/EKS, API Gateway, S3, DynamoDB, RDS, OpenSearch, SageMaker, and CloudWatch.
• Practical experience with AWS Bedrock for LLM‑powered applications and agents, including knowledge bases and guardrails.
• Experience implementing RAG and working with vector search / vector databases.
• Experience with CI/CD and infrastructure‑as‑code using tools such as Terraform and CloudFormation.
• Familiarity with MLOps/AIOps (for example, MLflow and SageMaker) and AI observability for 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 such as Kubernetes, AWS ECS, and AWS EKS.

Technologies

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

Desired

• Strong technical judgment and communication skills for explaining 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 such as LangSmith (or similar).
• Experience with advanced agent frameworks including CrewAI and AutoGen and 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 and DeepEval (or similar).