AI/ML Platform Engineer

SAIC•Reston, VA

1d•Remote

About The Position

SAIC is seeking an AI/ML Platform Engineer to join its Engineering Team supporting civilian government agency programs. The role focuses on building and operating governed 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 so customers can concentrate on their mission problems and applications. The engineer partners directly with customers from concept through production delivery, translating requirements into specifications, designs, implementations, and production handoffs. The role calls for a hands-on engineer who stays current with the evolving AI/ML landscape, evaluates and integrates new capabilities, and builds reusable platform patterns rather than one-off solutions. Work includes cloud-based AI/ML platforms and infrastructure, GenAI and agentic AI capabilities, traditional ML operationalization, security and governance controls, and repeatable onboarding through infrastructure as code.

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, including 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, including MLflow, Git, and Docker, and 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 integrate appropriate options into a platform.

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, knowledge bases, vector databases, embedding pipelines, tenant-specific 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 such as bias, fairness, explainability, and model cards, and fleet-wide drift and quality monitoring.
Partner directly with customers from concept through delivery, owning requirements, specifications, solution design, hands-on implementation, and production handoff.
Work with mission teams to understand their problems and help them build on the platform.
Evaluate new AI/ML tools and services and integrate suitable capabilities into the platform offering.
Build repeatable and portable platform onboarding patterns and IaC templates using Terraform or CloudFormation, with SSO integration.
Develop secure, API-based model access and inference endpoints consistent with governance and security standards.
Document reusable patterns, frameworks, and APIs for future deployments.