AI Engineer Level II

ChaTeck Incorporated Logo
ChaTeck Incorporated
Washington, DC, United States
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Role: AI Engineer Level II

Location:Washington, DC – Onsite

Position Summary

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.

Key Responsibilities

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.

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 : 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)

Qualifications

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

  • 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)

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 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.

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