AI Engineer Level II

Position Summary

Role: AI Engineer Level II

Location: Washington, DC – Onsite

As an AI Engineer (Level II), you’ll design and implement enterprise-grade AI systems with a focus on Retrieval-Augmented Generation (RAG),
agentic AI
, and cloud-native ML pipelines. You’ll 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, Auto Gen, Agent Framework, CrewAI, Agno, and Lang Chain.
• 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.

Software Engineering Core

• 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.

Cloud AI Tech Stack

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

Education
:
Bachelor’s in CS, Engineering, or equivalent hands-on expertise

Experience
: 5 years in software engineering; 2 years in GenAI/LLM applications (RAG, agents, safety, eval)

Certifications (Required)

• Microsoft Certified:
  Azure AI Fundamentals (AI-900)
• Microsoft Certified:
  Azure Data Fundamentals (DP-900)
• Responsible AI certifications
• AWS Machine Learning Specialty
• Tensor Flow 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)

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 Type Script 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):

• Lang Chain, 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 Dev Ops and AWS Code Pipeline.

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