AI/ML Platform Engineer

SAIC•Flexwork, VA

17h

About The Position

We are seeking an AI/ML Platform Engineer to join the Engineering Team supporting Civilian government agency programs. This role builds and operates platforms that enable mission teams to develop traditional machine learning, Generative AI, and Agentic AI solutions. The platform provides the tools, infrastructure, guardrails, governance, monitoring, and technical foundations that let teams focus on their mission problems. The engineer will deliver platform capabilities as a governed, multi-tenant enterprise offering and partner directly with customers from concept through production handoff. The role calls for a hands-on, proactive engineer who can translate customer needs into requirements and working solutions, evaluate and integrate emerging capabilities, and create reusable patterns for future deployments. The work takes place in a regulated environment and may involve partnering with AWS and Databricks.

Requirements

Bachelor’s or master’s degree in computer science, Machine Learning, or a related field.
At least 5 years of hands-on AI/ML engineering experience, including current coding, pipeline development, and systems architecture across traditional ML, GenAI, and/or agentic AI.
Experience enabling AI/ML capabilities as a managed service for a team or organization, such as governed multi-tenant compute, guarded model deployment pipelines, GenAI access layers, or agentic orchestration infrastructure.
Strong proficiency in Python and hands-on experience with core AI/ML libraries such as PyTorch, TensorFlow, HuggingFace, and LangChain or equivalent.
Hands-on experience with cloud-based AI/ML platforms such as Databricks, SageMaker, or equivalent, covering model development, MLOps, data governance, and multi-tenant operationalization.
Hands-on experience with GenAI and LLM frameworks, including RAG pipelines, vector databases, embedding models, prompt engineering, and governed model access layers.
Strong infrastructure-as-code and cloud engineering skills, including Terraform, CloudFormation, AWS-native services, enterprise SSO integration, and cloud security patterns.
Knowledge of federal security and governance standards, including FedRAMP, NIST 800-53, and data isolation best practices.
Ability to work directly with customers and translate ambiguous problem statements into requirements, specifications, and working solutions end to end.
Comfort working in regulated environments and with governance requirements and security standards.

Nice To Haves

Experience with agentic AI frameworks, including multi-agent orchestration, tool use, memory and state management, human-in-the-loop design, and safety boundary enforcement.
Familiarity with AI gateway patterns, such as LiteLLM, for centralized, governed LLM access with swappable model backends.
Experience with vector databases such as Pinecone, FAISS, Weaviate, or Amazon OpenSearch Vector.
Working knowledge of CI/CD and MLOps practices and tools such as MLflow, Git, and Docker, as well as data governance in multi-tenant environments.
Experience in federal, healthcare, financial services, or other regulated environments where governance and compliance are first-class engineering concerns.
Curiosity and judgment to evaluate emerging LLMs, open-source GenAI tooling, and agentic frameworks and bring appropriate capabilities into a platform context.

Responsibilities

Enable governed, multi-tenant AI/ML platform services, including tenant onboarding, entitlements, access controls, compute environments, and service-level guardrails across traditional ML, GenAI, and agentic AI.
Provide GenAI application building blocks, including governed access to frontier models through an AI gateway, RAG infrastructure, per-tenant guardrails, grounding validation, and feedback mechanisms.
Enable agentic AI patterns, including orchestration frameworks, tool and API integration, memory and state management, human-in-the-loop controls, and safety guardrails.
Enable traditional ML operationalization through governed training environments, model deployment pipelines with TAI controls, and fleet-wide drift and quality monitoring.
Partner with customers end to end, owning requirements, specifications, solution design, hands-on implementation, and production handoff.
Work directly with mission teams to understand their problems and help them build on the platform.
Evaluate emerging platform capabilities, assess tools and services, and integrate appropriate capabilities into the platform offering.
Build repeatable and portable platform onboarding patterns and infrastructure-as-code templates using Terraform or CloudFormation, including SSO integration.
Develop secure API-based model access and inference endpoints that meet governance and security standards.
Document reusable patterns, frameworks, and APIs for use in future deployments.