AI/ML Platform Engineer

SAIC

. Enable AI/ML platform services as a governed, multi-tenant enterprise offering across traditional ML, GenAI, and agentic AI.

Posted 10/15/2026full-timeRemote • Virginia • United StatesMid-LevelSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in AI/ML engineering, focusing on building and operationalizing multi-tenant platforms with strong governance and security standards. Proficient in developing and integrating advanced AI capabilities, including traditional ML, GenAI, and agentic AI frameworks.

Highest-signal resume keywords

AI/ML Engineering ExperiencePython ProficiencyTerraform and CloudFormation SkillsCloud-Based AI/ML Platforms ExperienceKnowledge of Federal Security Standards

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

AI/ML EngineeringModel Deployment PipelinesGoverned Multi-Tenant ComputePrompt EngineeringData GovernanceMLOps PracticesEmbedding ModelsVector DatabasesAgentic AI FrameworksOrchestration Frameworks

Soft Skills

Customer CollaborationProblem-Solving

Tools & Technologies

PyTorchTensorFlowHuggingFaceDatabricksSageMakerAWS-Native ServicesMLflowGitDockerEnterprise SSO Integration

Industry Keywords

Governance StandardsFedRAMPNIST 800-53Data Isolation Best PracticesRegulated Environments

Tech Stack

Tools & technologies

AWSCloudDockerPythonPyTorchTensorflowTerraform

About the role

Key responsibilities & impact

Enable AI/ML platform services as a governed, multi-tenant enterprise offering across traditional ML, GenAI, and agentic AI.
Provide governed access to frontier models through an AI gateway layer, RAG infrastructure, knowledge bases, vector databases, embedding pipelines, per-tenant guardrails, grounding validation, and feedback mechanisms.
Enable agentic AI patterns including orchestration frameworks, tool/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 directly with customers from concept through production handoff, owning requirements, specifications, solution design, implementation, and delivery.
Evaluate and integrate new AI/ML platform capabilities, tools, and services.
Build repeatable and portable platform onboarding patterns and IaC templates using Terraform and 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.

Requirements

What you’ll need

Bachelor’s or master’s degree in computer science, Machine Learning, or a related field.
5+ years of current, hands-on AI/ML engineering experience actively writing code, building pipelines, and architecting systems across traditional ML, GenAI, and/or agentic AI.
Demonstrated platform-level thinking and experience enabling AI/ML capabilities as a managed service, including governed multi-tenant compute, model deployment pipelines with guardrails, GenAI access layers, or agentic orchestration infrastructure.
Strong proficiency in Python and hands-on experience with PyTorch, TensorFlow, HuggingFace, 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, RAG pipelines, vector databases, embedding models, prompt engineering, and governed model access layers.
Strong IaC and cloud engineering skills with 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.
Proven ability to work directly with customers and translate ambiguous problem statements into requirements, specifications, and working solutions end to end.
Comfortable working in regulated environments with governance requirements and security standards.
Desired: experience with agentic AI frameworks, AI gateway patterns such as LiteLLM, vector databases including Pinecone, FAISS, Weaviate, or Amazon OpenSearch Vector, CI/CD and MLOps practices including MLflow, Git, and Docker, and regulated-industry experience.