Mindlance

Mindlance

AI Engineer

Washington, District of Columbia, USA · third party, contract

Sponsorship not specifiedDetected 78 days ago
TypeScriptPythonC#.NETDistributed SystemsData StructuresAlgorithmsRedisDatabricksVector DatabasesAWSAzureDockerKubernetesCI/CDDevOpsMachine LearningTensorFlowSparkData EngineeringData ScienceNLPLLMsRAG

About the role

  • Title: AI Engineer Standard III Duration: 3 Months - Long Term Location: Washington, DC 20433 Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon.
  • Provide tool functions with RBAC scopes, schema versioning, rate limiting, request/response validation, and audit trails.
  • Deploy Azure AI Agent Service (AGA) patterns for agent registry/broker/governance with agent telemetry and policy enforcement.

Responsibilities

  • Architect and Implement AI Solutions
  • clean architecture, design patterns, SOLID principles, unit/integration/e2e tests, testing pyramids.
  • Agile ceremonies, RACI clarity, cross-functional delivery with product/design/data/security.
  • Integrate and Operate AI Infrastructure Implement Model Context Protocol (MCP) servers integrating with project related areas.
  • Develop and Manage

Nice to have

  • C# and Python (production-grade),.Net, plus TypeScript for service/UI when needed.
  • Azure & AWS services (see Knowledge Requirements) with hands-on implementation and operations.
  • Languages: C# and Python (production-grade),.Net, plus TypeScript for service/UI when needed.
  • Desired Skills/Abilities (not required but a plus): LangChain, Hugging Face, MLflow
  • Kubernetes + GPU scheduling
  • vector search tuning (HNSW/IVF).

Skills

  • chunking, embeddings, hybrid/semantic ranking, re-ranking, evaluation, and citation display.
  • Operate large-scale vectorization with quality gates and drift monitoring.
  • Know how to apply agent governance and MCP-based controls across heterogeneous agents and runtimes (register, observe, govern, retire).
  • Implement CI/CD with automated tests, security scans.
  • Have knowledge on how to secure model workloads.
  • Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, LangChain.
  • LangChain, Hugging Face, MLflow; Kubernetes + GPU scheduling; vector search tuning (HNSW/IVF).
  • Hybrid/multi-cloud deployments using Azure Arc and AWS Outposts; CI/CD for AI workloads across Azure DevOps and AWS CodePipeline.

Benefits

  • Azure OpenAI, Llama (Meta), Claude, etc.., and task-specific OSS models (vision, speech), with policy-driven model routing for performance, safety, and cost.

Equal opportunity

  • Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.
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This listing is sourced directly from Mindlance's careers page and normalized into a canonical job model.