Role: AI Engineer Standard I
Location: Washington, DC
Duration: 6+ Months Long Term
Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon.
Position Overview:
The AI Engineer designs, builds, and operates secure, scalable AI systems that advance the organization s digital strategy. The role centers on Retrieval-Augmented Generation (RAG) pipelines, agentic AI (including Azure AI Agent Service and Model Context Protocol), and enterprise-grade service delivery across Azure and AWS. The AI Engineer partners with product, platform, data, and security teams to deliver robust, compliant, and cost-efficient AI capabilities.
Essential Job Functions:
" Architect and Implement AI Solutions
oDesign and build RAG pipelines using Azure AI/Search and vector databases: chunking, embeddings, hybrid/semantic ranking, re-ranking, evaluation, and citation display.
oBuild enterprise conversational systems (multi-turn, retrieval-grounded) with prompt lifecycle management, guardrails, audit logging, and telemetry.
oSupport multiple LLMs and modalities: Azure OpenAI, Llama (Meta), Claude, etc.., and task-specific OSS models (vision, speech), with policy-driven model routing for performance, safety, and cost.
" Integrate and Operate AI Infrastructure
oImplement Model Context Protocol (MCP) servers integrating with project related areas.
oProvide tool functions with RBAC scopes, schema versioning, rate limiting, request/response validation, and audit trails.
oDeploy Azure AI Agent Service (AGA) patterns for agent registry/broker/governance with agent telemetry and policy enforcement.
oUse Azure Batch for large-scale, parallel inferencing/vectorization jobs; leverage AWS EMR for distributed data/feature processing in AI pipelines.
" Develop and Manage Data Pipelines
o Build ingestion and enrichment for RAG connectors and ETL/ELT: document normalization, PII redaction, metadata enrichment, SLA/SLO monitoring, and lineage.
o Operate large-scale vectorization with quality gates and drift monitoring.
o Use Azure Data Factory (ADF) and Azure Databricks for orchestrated, scalable data processing; use AWS EMR for Hadoop/Spark workloads supporting AI features.
" Build Agentic AI Solutions
oDesign secure tool-calling and multi-agent orchestration using Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, and LangChain or others.
oKnow how to apply agent governance and MCP-based controls across heterogeneous agents and runtimes (register, observe, govern, retire).
"Model Evaluation and Optimization
oEvaluate and fine-tune open-source and proprietary models; optimize for quality, latency, safety, and cost with A/B and offline eval suites.
oImplement CI/CD with automated tests, security scans. Have knowledge on how to secure model workloads.
Software Engineering Emphasis (Core)
" CS fundamentals: algorithms, data structures, complexity, distributed systems, networking, concurrency.
" SDLC excellence: clean architecture, design patterns, SOLID principles, unit/integration/e2e tests, testing pyramids.
" Secure coding & threat modeling for AI apps: input validation, sandboxed tool functions, secrets hygiene, role-based access & least privilege.
" Performance engineering: profiling, caching, vector index tuning, latency/throughput optimization, and cost controls (token/embedding/compute).
" Collaboration & Delivery: Agile ceremonies, RACI clarity, cross-functional delivery with product/design/data/security.
Knowledge Requirements Cloud AI Tech Stack (Azure & AWS)
" Azure: Azure OpenAI; Azure AI/Search; Azure Machine Learning; Azure Kubernetes Service (AKS); Azure Functions; Azure API Management; Key Vault; Event Hub; App Insights; Log Analytics; Azure Batch; Azure Data Factory (ADF); Azure Databricks.
" AWS: Amazon SageMaker; AWS Bedrock; Amazon Kendra; Amazon Comprehend; AWS Lambda; Amazon API Gateway; AWS Secrets Manager; Amazon S3; Amazon CloudWatch; Elastic Kubernetes Service (EKS); Amazon EMR.
" Vector DBs & Indexing: Azure AI Search vector storage, Redis, FAISS/HNSW; hybrid search + semantic ranking.
" Frameworks: Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, LangChain.
" Local/Edge Inference: running models locally via Docker/Ollama/vLLM/Triton; GPU provisioning; quantization (GGUF) for Llama-family models.
Educational Qualifications and Experience:
"Education: Bachelor s degree in Computer Science, Engineering, Information Technology, Data Science or equivalent hands-on expertise.
"Experience: 6+ years of software engineering experience, with at least 2+ years in applied LLM/GenAI (RAG, agents, eval, safety).
Certification Requirements:
Mandatory:
" Microsoft Certified: Azure AI Fundamentals (AI-900)
" Microsoft Certified: Azure Data Fundamentals (DP-900)
" Responsible AI certifications
" AWS Machine Learning Specialty
" TensorFlow Developer
" Kubernetes CKA/CKAD
" SAFe Agile Software Engineering (ASE)
Additional Value (Preferred):
" Microsoft Certified: Azure AI Engineer Associate (AI-102)
" Microsoft Certified: Azure Data Scientist Associate (DP-100)
" Microsoft Certified: Azure Solutions Architect Expert (AZ-305)
" Microsoft Certified: Azure Developer Associate (AZ-204)
Required Skills/Abilities:
" GenAI architecture mastery: RAG, vector DBs, embeddings, transformer internals, multi-modal pipelines.
" Agentic systems: Azure AI Agent Service patterns, MCP servers, registry/broker/governance, secure tool-calling.
" Languages: 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.
" Model ops: eval suites, safety tooling, fine-tuning, guardrails, traceability.
" Business & delivery: solution architecture, stakeholder alignment, roadmap planning, measurable impact.
Desired Skills/Abilities (not required but a plus):
"LangChain, Hugging Face, MLflow; Kubernetes + GPU scheduling; vector search tuning (HNSW/IVF).
"Responsible AI: policy mapping, red-team playbooks, incident response for AI.
"Hybrid/multi-cloud deployments using Azure Arc and AWS Outposts; CI/CD for AI workloads across Azure DevOps and AWS CodePipeline.
Experience Matrix for Levels:
"Level I: 2+ years of experience
"Level II: 5+ years of experience
"Level III: 8+ years of experience
EEO:
Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.