Mid AI/ML Engineer

Snowrelic Inc; Washington (District of Columbia); Hybrid; USD 120,000 - 170,000; Full time

Job summary

Snowrelic Inc. in Washington, DC hybrid environment seeks an AI/ML engineer to design and deploy production-grade AI systems. You will lead end-to-end work from data prep to model deployment and monitoring.

You will build, test and scale AI applications with a strong focus on security, governance and regulatory compliance. 3+ years in AI/ML with hands-on Python, APIs and cloud delivery are required.

Qualifications

• 3+ years relevant experience, including 2+ years AI/ML implementation.
• Hands-on Python, APIs, data pipelines, Git, testing and cloud delivery.
• Experience with LLM, RAG, NLP, document intelligence or predictive ML.
• Build AI applications, automation, integrations and production solutions.

Responsibilities

• Build AI applications, automation, integrations and production solutions.
• Experience with MLOps/DevSecOps, CI/CD, monitoring and evaluation.
• Knowledge of security, PII, accessibility and regulated environments.
• Develop and deploy scalable AI/ML pipelines and tooling.

Skills

Python

APIs

Data pipelines

Git

Testing

Cloud delivery

Job description

Location: Washington, DC Hybrid (3 days onsite)

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. Evidence must be supplied at onboarding and annually thereafter; self-attestation is not sufficient.

• 3+ years relevant experience, including 2+ years AI/ML implementation.
• Hands-on Python, APIs, data pipelines, Git, testing and cloud delivery.
• Experience with LLM, RAG, NLP, document intelligence or predictive ML.
• Build AI applications, automation, integrations and production solutions.
• Experience with MLOps/DevSecOps, CI/CD, monitoring and evaluation.
• Knowledge of security, PII, accessibility and regulated environments.

Preferred Qualifications

• Experience with AWS, Azure, Google Cloud, or FedRAMP-authorized AI services.
• Experience with vector databases, embedding pipelines, reranking, evaluation tooling, and LLM observability.
• Experience supporting Federal ATO/RMF, SBOM creation, vulnerability scanning, and Section 508 remediation.
• Experience in OSHA, grants oversight, workforce programs, compensation claims, fraud detection, document processing, or public-facing information systems.

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. Evidence must be supplied at onboarding and annually thereafter; self-attestation is not sufficient.