What are the responsibilities and job description for the Senior AI/ML Engineer – Forward Deployed Engineer position at Snowrelic Inc?
Senior AI/ML Engineer Forward Deployed Engineer
Location: Washington, DC Hybrid (3 days onsite)
Security eligibility: US Citizen or Permanent Resident (no H1 or OPT) Must be eligible for and obtain DOL onboarding approval, NACI clearance, PIV, system access, mandatory training completion, and execution of DOL Rules of Behavior and NDA.
Note
Required certification evidence
The candidate must hold and provide verifiable evidence of at least one relevant AI/ML credential, such as an Azure AI/Generative AI certification, AWS Generative AI or ML certification, Google Cloud Generative AI/ML credential, Databricks Generative AI Engineer credential, Snowflake data credential, NVIDIA generative AI credential, CAIP, or equivalent.
Skills
The candidate must hold and provide verifiable evidence of at least one relevant AI/ML credential, such as an Azure AI/Generative AI certification, AWS Generative AI or ML certification, Google Cloud Generative AI/ML credential, Databricks Generative AI Engineer credential, Snowflake data credential, NVIDIA generative AI credential, CAIP, or equivalent. Evidence must be supplied at onboarding and annually thereafter; self-attestation is not sufficient.
Location: Washington, DC Hybrid (3 days onsite)
Security eligibility: US Citizen or Permanent Resident (no H1 or OPT) Must be eligible for and obtain DOL onboarding approval, NACI clearance, PIV, system access, mandatory training completion, and execution of DOL Rules of Behavior and NDA.
Note
Required certification evidence
The candidate must hold and provide verifiable evidence of at least one relevant AI/ML credential, such as an Azure AI/Generative AI certification, AWS Generative AI or ML certification, Google Cloud Generative AI/ML credential, Databricks Generative AI Engineer credential, Snowflake data credential, NVIDIA generative AI credential, CAIP, or equivalent.
Skills
- 7 years AI/ML, software, cloud, data engineering or architecture experience.
- 2 years production experience with GenAI, LLM, RAG, AI Agents or AI operations.
- Build secure AI solutions for Federal/DOL agencies from discovery through production.
- Strong Python, APIs, data pipelines, cloud, MLOps/LLMOps.
- Experience with LLM integration, RAG, NLP, document intelligence, agents/MCP.
- AI evaluation, security/adversarial testing, Responsible AI and compliance.
- Federal/regulated environment experience strongly preferred.
- Relevant AI/ML certification required.
- Direct experience in federal civilian agencies, especially systems involving PII, CUI, public-facing digital services, grants, enforcement, benefits, claims, or case-management operations.
- Experience with AWS, Azure AI Foundry, Google Vertex AI, AWS Bedrock, GitHub Copilot/VS Code, GitLab, JIRA, and Government-controlled CI/CD pipelines.
- Experience with accessibility-by-design and delivery of systems meeting Section 508/WCAG 2.1 A/AA.
- Experience supporting independent testing, red teaming, security assessment, and production ATO activities.
The candidate must hold and provide verifiable evidence of at least one relevant AI/ML credential, such as an Azure AI/Generative AI certification, AWS Generative AI or ML certification, Google Cloud Generative AI/ML credential, Databricks Generative AI Engineer credential, Snowflake data credential, NVIDIA generative AI credential, CAIP, or equivalent. Evidence must be supplied at onboarding and annually thereafter; self-attestation is not sufficient.