AI Engineer Level III

Role: AI Engineer Level III
Location: Washington DC, Onsite

Position Summary

As a senior AI Engineer, you will architect and lead the delivery of scalable, secure GenAI systems with enterprise-grade performance. Your focus will include RAG pipelines, agentic orchestration, and cloud-native ML infrastructure across Azure and AWS. You'll own solution architecture, direct engineering execution, and align technical delivery to strategic business outcomes.

Key Responsibilities

AI Solution Architecture & Delivery

• Lead end-to-end design of RAG pipelines using Azure AI/Search and vector DBs (Redis, FAISS, HNSW).
• Deliver multi-turn, retrieval-grounded conversational systems with robust prompt lifecycle, guardrails, and telemetry.
• Drive integration of multi-modal LLMs (Azure OpenAI, Claude, Llama, OSS models) with dynamic model routing for cost and safety.

AI Infrastructure Leadership

• Architect and deploy Model Context Protocol (MCP) servers with RBAC, versioning, audit logging, validation, and rate limiting.
• Build policy-compliant agent ecosystems using Azure AI Agent Service: registry, broker, telemetry, governance enforcement.
• Manage high-throughput inferencing pipelines using Azure Batch and distributed AI data flows with AWS EMR.

Enterprise Data & Feature Pipelines

• Oversee RAG data ingestion and enrichment: doc normalization, PII redaction, metadata tagging, SLA/SLO monitoring, lineage.
• Lead vectorization workflows with drift monitoring and quality gates.
• Architect and optimize Azure Data Factory, Databricks, and AWS EMR data engineering for scalable AI features.

Agentic AI Systems

• Engineer and govern secure tool-calling and multi-agent orchestration using Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, LangChain.
• Enforce MCP-based controls for heterogeneous agents across runtimes, ensuring safety and traceability.

Model Operations & Governance

• Evaluate, fine-tune, and optimize models for quality, safety, cost, and latency using A/B and offline evaluation suites.
• Define CI/CD pipelines for AI workloads including automated tests, scans, safety tools, and trace logging.
• Ensure security posture of AI/LLM workloads via threat modeling and secure software practices.

Engineering & Leadership Core

• Strong CS fundamentals: distributed systems, concurrency, networking, complexity.
• Expert-level SDLC: clean architecture, SOLID, layered testing, DevSecOps.
• Secure AI app development: sandboxed tools, secrets hygiene, RBAC.
• Performance engineering: latency profiling, cost tuning (token, embedding, GPU), vector DB indexing.
• Lead agile ceremonies, cross-functional delivery, and roadmap execution with RACI clarity.

Cloud AI Tech Stack

Azure: Azure OpenAI, Azure AI/Search, AML, AKS, Azure Batch, ADF, Azure Databricks, Azure Functions, API Management, Key Vault, App Insights
AWS: SageMaker, Bedrock, Lambda, API Gateway, Comprehend, S3, CloudWatch, EMR, EKS
Vector DBs: Azure AI Search, Redis, FAISS/HNSW
Frameworks: Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno, LangChain
Inference: Docker/Ollama, vLLM, Triton, quantized Llama (GGUF), edge inference, GPU provisioning

Qualifications

Education: Bachelor's in CS, Engineering, or related; Master's preferred
Experience: 8+ years in software engineering, 2+ in applied GenAI (RAG, agent systems, model safety/eval)

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.

Certifications (Required)

• Azure AI Fundamentals (AI-900) & Data Fundamentals (DP-900)
• Responsible AI Certifications
• AWS Machine Learning Specialty
• TensorFlow Developer
• Kubernetes CKA or CKAD
• SAFe Agile Software Engineering

Preferred:

• Azure AI Engineer Associate (AI-102)
• Azure Data Scientist (DP-100)
• Azure Solutions Architect Expert (AZ-305)
• Azure Developer Associate (AZ-204)

Ready to lead AI at scale? Apply now and architect the future of enterprise intelligence.