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

Why This Role?

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

Role Summary

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.

Key Responsibilities

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, AutoGen, CrewAI, LangChain

Apply governance and lifecycle management for agent runtimes

Fine-tune models, conduct A/B testing, and implement CI/CD pipelines with validation

Core Competencies

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

Required Skills

Expertise in RAG, embeddings, transformer models, and multi-modal pipelines

Production-level C#, Python, .NET; TypeScript 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

Tools & Platforms

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

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

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

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

Inference: Docker/Ollama, vLLM, GGUF quantization, GPU provisioning

Required Certifications

Microsoft Certified: Azure AI Fundamentals (AI-900)

Microsoft Certified: Azure Data Fundamentals (DP-900)

Responsible AI certification

AWS Machine Learning Specialty

TensorFlow Developer

Kubernetes CKA/CKAD

SAFe Agile Software Engineering

Preferred (Bonus)

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 DevOps, AWS CodePipeline), hybrid deployments (Azure Arc, AWS Outposts)

Step into a role where AI meets cloud scalability.

Apply now and help shape tomorrow’s AI systems.