7 days ago
Remote, United StatesSenior
Responsibilities
- Build and operate governed, multi-tenant AI/ML platform services covering tenant onboarding, access controls, compute environments, and service-level guardrails.
- Develop GenAI platform capabilities including AI gateway access, RAG infrastructure, vector databases, embedding pipelines, grounding validation, and feedback mechanisms.
- Build agentic AI capabilities for orchestration, tool and API integration, memory and state management, human-in-the-loop controls, and safety guardrails.
- Operationalize traditional ML through governed training environments, model deployment pipelines, TAI controls, and fleet-wide drift and quality monitoring.
- Partner directly with customers from concept through requirements, specifications, solution design, hands-on implementation, and production handoff.
- Evaluate and integrate emerging AI/ML tools and services into the platform offering.
- Create repeatable Terraform and CloudFormation onboarding patterns with SSO integration and deployment portability.
- Develop secure API-based model access and inference endpoints and document reusable platform patterns, frameworks, and APIs.
Requirements
- Bachelor’s or master’s degree in computer science, machine learning, or a related field.
- At least 5 years of current, hands-on AI/ML engineering experience writing code, building pipelines, and architecting systems across traditional ML, GenAI, and/or agentic AI.
- Experience enabling AI/ML capabilities as a managed, governed service, including multi-tenant compute, model deployment pipelines, GenAI access layers, or agentic orchestration infrastructure.
- Strong Python skills and hands-on experience with AI/ML libraries such as PyTorch, TensorFlow, HuggingFace, and LangChain or equivalent.
- Experience with cloud AI/ML platforms such as Databricks or SageMaker, including model development, MLOps, data governance, and multi-tenant operations.
- Experience with GenAI and LLM frameworks, RAG pipelines, vector databases, embedding models, prompt engineering, and governed model access layers.
- Strong infrastructure-as-code and cloud engineering skills with Terraform, CloudFormation, AWS-native services, enterprise SSO integration, and cloud security patterns.
- Knowledge of FedRAMP, NIST 800-53, and data isolation best practices.
- Ability to work directly with customers and translate ambiguous problems into requirements, specifications, and working solutions.
- Experience with agentic AI frameworks, multi-agent orchestration, tool use, memory and state management, human-in-the-loop design, and safety boundary enforcement is desired.
- Familiarity with AI gateway patterns such as LiteLLM and experience with vector databases such as Pinecone, FAISS, Weaviate, or Amazon OpenSearch Vector is desired.
- Working knowledge of CI/CD and MLOps practices, including MLflow, Git, and Docker, is desired.
- Experience in federal, healthcare, financial services, or other regulated environments is desired.
- Curiosity and judgment to evaluate emerging LLMs, open-source GenAI tooling, and agentic frameworks is desired.
Categories
About SAIC
SAIC (Science Applications International Corporation) is a government technology integrator delivering systems engineering, enterprise IT, secure cloud, cybersecurity, C5ISR, and data analytics for U.S. defense, space, intelligence, and civilian agencies. It builds and sustains mission IT and digital engineering solutions under multi-year federal contracts and task orders. Founded in 1969, SAIC is headquartered in Reston, Virginia, and is a public company traded on the NYSE under the ticker SAIC.