What are the responsibilities and job description for the Mid AI/ML Engineer position at Digital Links Inc?
Location: Washington, DC Hybrid (3 days onsite)
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.
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.
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.
- 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.
- 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.
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.