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.

Essential Job Functions:

Architect and Implement AI Solutions

Design and build RAG pipelines using Azure AI/Search and vector databases: chunking, embeddings, hybrid/semantic ranking, re-ranking, evaluation, and citation display.
Build enterprise conversational systems (multi-turn, retrieval-grounded) with prompt lifecycle management, guardrails, audit logging, and telemetry.
Support 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

Implement Model Context Protocol (MCP) servers integrating with project related areas.
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.
Use 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

Build ingestion and enrichment for RAG connectors and ETL/ELT: document normalization, PII redaction, metadata enrichment, SLA/SLO monitoring, and lineage.
Operate large-scale vectorization with quality gates and drift monitoring.
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

Design secure tool-calling and multi-agent orchestration using Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, and LangChain or others.
Know how to apply agent governance and MCP-based controls across heterogeneous agents and runtimes (register, observe, govern, retire).

Model Evaluation and Optimization

Evaluate and fine-tune open-source and proprietary models; optimize for quality, latency, safety, and cost with A/B and offline eval suites.
Implement 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 modelling 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:

Additional Value (Preferred):

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.

Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.

AI Engineer

Key skills