AI Engineer – Level II

Role: AI Engineer Level II
Location: Washington DC - Onsite

As an AI Engineer (Level II) youll design and implement enterprise-grade AI systems with a focus on Retrieval-Augmented Generation (RAG) agentic AI and cloud-native ML pipelines. Youll work cross-functionally to operationalize secure scalable solutions across Azure and AWS platforms contributing to production-ready multi-modal GenAI applications.

AI Architecture & Delivery

• Design and deploy RAG pipelines using Azure AI/Search and vector DBs (Redis FAISS HNSW).

• Develop conversational AI systems with prompt lifecycle management telemetry and guardrails.

• Integrate LLMs like Azure OpenAI Llama Claude and OSS models across vision and speech domains.

Infrastructure & Orchestration

• Implement Model Context Protocol (MCP) servers with RBAC schema versioning validation and audit trails.

• Deploy Azure AI Agent Service patterns: agent registry policy enforcement and telemetry logging.

• Use Azure Batch and AWS EMR for parallel inferencing and distributed feature processing.

Data Pipeline Engineering

• Build and manage ingestion pipelines: document normalization metadata enrichment PII redaction SLA monitoring.

• Operate scalable vectorization pipelines with drift detection and quality gates.

• Use Azure Data Factory and Databricks; AWS EMR for large-scale Hadoop/Spark workloads.

Agentic AI Development

• Implement secure tool-calling and multi-agent orchestration using Semantic Kernel AutoGen Agent Framework CrewAI Agno and LangChain.

• Apply governance telemetry and lifecycle management across agent runtimes with MCP controls.

Model Ops & Evaluation

• Fine-tune and evaluate OSS and proprietary models; conduct A/B tests and latency/cost analysis.

• Implement CI/CD pipelines with security scans and validation for AI/LLM workloads.

• Proficiency in CS fundamentals: algorithms distributed systems concurrency networking.

• Experience with SDLC excellence: clean architecture SOLID testing pyramids (unit integration E2E).

• Secure AI app development: input validation secret hygiene RBAC sandboxed functions.

• Performance engineering: latency tuning token optimization vector index profiling.

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

Education: Bachelors in CS Engineering or equivalent hands-on expertise
Experience: 5 years in software engineering; 2 years in GenAI/LLM applications (RAG agents safety eval)

• 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 or CKAD

• SAFe Agile Software Engineering

Preferred:

• Azure AI Engineer Associate (AI-102)

• Azure Data Scientist Associate (DP-100)

• Azure Solutions Architect (AZ-305)

• Azure Developer Associate (AZ-204)

• 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.

Step into a high-impact role where AI meets cloud scalability. Apply now and bring cutting-edge solutions to life.