AI Engineer Level II

Overview

Benefits:

Competitive salary

Opportunity for advancement

Training & development

AI Engineer – Level II

Location:

Washington, DC (Onsite)

Experience:

5+ years in software engineering | 2+ years in GenAI/LLM systems

Join a high-impact AI team building secure, scalable GenAI systems. Gain exposure to:

• Cutting-edge RAG and agentic AI architectures
• Azure and AWS AI ecosystems
• Multi-modal LLM integration across vision and speech
• Production-grade CI/CD for AI/ML workloads
• Fast-tracked certifications and career growth

As an AI Engineer (Level II), you’ll design, implement, and optimize enterprise-scale AI systems. You’ll lead architecture, agent orchestration, and model integration while collaborating with cross-functional teams to deliver production-ready solutions.

• AI Architecture & Delivery
  • Design RAG pipelines using Azure AI/Search, Redis, FAISS, HNSW
  • Build conversational systems with prompt lifecycle management and telemetry
  • Integrate LLMs like Azure OpenAI, Claude, Llama, and open-source models
• Infrastructure & Orchestration
  • Deploy Model Context Protocol (MCP) servers with RBAC and audit trails
  • Implement Azure AI Agent Service patterns for agent registry and policy enforcement
  • Use Azure Batch and AWS EMR for scalable inferencing and processing
• Data Pipeline Engineering
  • Build ingestion pipelines with PII redaction, metadata enrichment, SLA tracking
  • Operate vectorization pipelines with quality gates and drift detection
  • Leverage ADF, Databricks, and EMR for scalable workflows
• Agentic AI & Model Ops
  • Orchestrate multi-agent workflows using Semantic Kernel, Auto Gen, CrewAI, Lang Chain
  • Apply governance and lifecycle management for agent runtimes
  • Fine-tune models, conduct A/B testing, and implement CI/CD pipelines with validation

• Strong CS fundamentals: distributed systems, algorithms, concurrency, networking
• SDLC excellence: clean architecture, SOLID principles, testing frameworks
• Secure development: input validation, secret hygiene, sandboxing
• Performance tuning: latency optimization, vector index profiling

• Expertise in RAG, embeddings, transformer models, and multi-modal pipelines
• Production-level C#, Python, .NET;
  Type Script for service/UI (as needed)
• Experience with Azure and AWS AI tools and operations
• Familiarity with fine-tuning, safety tooling, model traceability
• Strong delivery skills: architecture, stakeholder alignment, roadmap execution

• Azure:
  OpenAI, AI Search, AML, AKS, ADF, Azure Batch, Databricks, Key Vault
• AWS:
  Sage Maker, Bedrock, EMR, Lambda, API Gateway, S3, EKS, Comprehend
• Vector DBs:
  Redis, FAISS, HNSW, Azure AI Search
• Frameworks:
  Lang Chain, Semantic Kernel, Auto Gen, Microsoft Agent Framework, CrewAI, Agno
• Inference:
  Docker/Ollama, vLLM, GGUF quantization, GPU provisioning

• Microsoft Certified:
  Azure AI Fundamentals (AI-900)
• Microsoft Certified:
  Azure Data Fundamentals (DP-900)
• Responsible AI certification
• AWS Machine Learning Specialty
• Tensor Flow Developer
• Kubernetes CKA/CKAD
• SAFe Agile Software Engineering

• Azure AI Engineer (AI-102), Data Scientist (DP-100), Architect (AZ-305), or Developer (AZ-204)
• Experience with MLflow, Hugging Face, vector tuning (HNSW/IVF)
• Responsible AI playbooks, incident response frameworks
• CI/CD for AI (Azure Dev Ops, AWS Code Pipeline), hybrid deployments (Azure Arc, AWS Outposts)

Step into a role where AI meets cloud scalability.

Apply now and help shape tomorrow’s AI systems.