Position Summary:
Title: AI Engineer Standard II
Duration: 6 Months – Possible Extension
Location: Washington, DC 20433
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
- 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 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).
“Mindlance is an Equal Opportunity Employer and does not discriminate in employment on the basis of – Minority/Gender/Disability/Religion/LGBTQI/Age/Veterans.”
