Benefits:

Competitive salary

Opportunity for advancement

Training & development

AI Engineer – Level III

Location: Washington, DC (Onsite) Experience: 8+ years in software engineering | 2+ years in applied GenAI

Why This Role?

Step into a senior engineering role where you’ll shape high-impact GenAI systems. Lead innovation across:

RAG pipelines and multi-agent orchestration

Azure and AWS-based GenAI platforms

Model governance, safety, and CI/CD for LLM workloads

Multi-modal model deployment at scale

Strategic delivery aligned with real-world enterprise impact

Role Summary

As a Level III AI Engineer, you’ll own the architecture and execution of secure, scalable AI systems. You will lead technical delivery across RAG pipelines, agent frameworks, model ops, and cloud-based ML workflows. This role demands deep hands-on expertise and cross-functional leadership.

Key Responsibilities

AI Architecture & Delivery

Lead design of RAG pipelines using Azure AI Search, Redis, FAISS, HNSW

Deliver multi-turn conversational systems with prompt lifecycle, telemetry, and guardrails

Integrate LLMs (Azure OpenAI, Claude, Llama, OSS) with dynamic routing for cost/safety balance

Infrastructure & Orchestration

Deploy MCP servers with RBAC, audit logging, version control, and rate limiting

Implement agent frameworks using Azure AI Agent Service (registry, policy enforcement, telemetry)

Operate large-scale inferencing via Azure Batch and AWS EMR

Data & Feature Pipelines

Lead ingestion pipelines: document normalization, metadata tagging, PII redaction, SLA/SLO tracking

Operate vectorization workflows with drift detection and quality gates

Architect scalable data flows using ADF, Databricks, and EMR

Agentic AI Development

Orchestrate multi-agent systems with Semantic Kernel, AutoGen, CrewAI, LangChain, Agno

Govern agent runtimes using MCP controls for security and traceability

Model Ops & Governance

Evaluate and fine-tune models; run A/B testing and latency-cost analysis

Build secure CI/CD pipelines with integrated testing, scans, and trace logging

Enforce DevSecOps and AI threat modeling for LLM workloads

Core Skills

Deep CS knowledge: distributed systems, concurrency, performance tuning

Expert in SDLC: clean architecture, SOLID, layered testing, DevSecOps practices

Secure AI app delivery: sandboxed tools, secrets hygiene, token/cost profiling

Agile leadership: drive sprints, lead technical planning, manage RACI across teams

Required Skills & Tools

Proficient in GenAI systems: embeddings, transformer models, vector DB indexing

Production-level expertise in Python, C#, .NET, and TypeScript (as needed)

Hands-on with Azure and AWS tools: AML, AKS, Databricks, SageMaker, EMR, EKS

Strong in model traceability, safety tooling, fine-tuning, and runtime observability

Strategic execution: solution architecture, roadmap alignment, delivery metrics

Tech Stack

Azure: Azure OpenAI, AI Search, AML, AKS, ADF, Azure Batch, Databricks, Key Vault, App Insights

AWS: SageMaker, Bedrock, Lambda, EMR, API Gateway, Comprehend, S3, CloudWatch, EKS

Vector DBs: Redis, FAISS, HNSW, Azure AI Search

Frameworks: LangChain, Semantic Kernel, AutoGen, Microsoft Agent Framework, CrewAI, Agno

Inference: Docker/Ollama, vLLM, Triton, GGUF quantization, GPU provisioning, edge AI

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 Certifications

Azure AI Engineer (AI-102)

Azure Data Scientist (DP-100)

Azure Solutions Architect (AZ-305)

Azure Developer Associate (AZ-204)

Ready to lead AI at scale?

Apply now and help architect the future of enterprise intelligence.