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. Youll 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: Bachelors in CS Engineering or related; Masters 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.