Generative AI Engineer

EXL; Pittsburgh (Allegheny County); Hybrid; USD 150,000 - 190,000; Full time

Benefits offered by this job

Hybrid work arrangement

Job summary

EXL is seeking a Sr. AI Engineer with hands-on experience in Semantic Kernel and Microsoft Agent Framework, coupled with Python/Django expertise to rebuild a unified orchestration layer on Azure AI services.

The role is based in Pittsburgh with a hybrid work model (2-3 days in-office). The candidate will work on moving SK plugins, planners, function calling and memory to the Agent Framework, while preserving API contracts, RBAC, and security.

Qualifications

• Must-have SK + Agent Framework hands-on refactoring experience.
• Python/Django to rebuild orchestration layer without breaking API contracts, RBAC, and permissions.
• Azure AI Services / RAG including Azure Search, OpenAI embeddings, retrieval, reranking, and citation paths.
• OpenShift / containerization to deploy migrated microservices on OCP.
• CI/CD/DevSecOps practices including PR-based workflows and vulnerability remediation.
• Observability and evaluation using OpenTelemetry instrumentation and related tooling.

Skills

Semantic Kernel

Microsoft Agent Framework

Python Django

Azure AI Services

OpenShift

Observability

CI/CD DevSecOps

Redis Enterprise

Azure APIM

RBAC Permissions

Tools

GitHub

CI/CD Tooling

Job description

Role : Sr. AI Engineer with Semantic Kernel + Microsoft Agent Framework exp. (Azure AI)

Location : Pittsburgh, PA

Work Type : Hybrid work setup (2-3 days a week work from office)

Hire Type : Full-Time with EXL

Experience : 7+ Years

Job Requirement:

Migration Context - Must Have

Knowledge Search is a containerized, enterprise-grade RAG platform on Azure AI Services with a Django/Python, deployed on OpenShift.

Semantic Kernel is the current orchestration layer, and the SK to MS Agent Framework migration is a Q3 '26 roadmap item that unblocks Agentic Workflows and the Control Panel.

The refactor re-platforms SK Native Plugins, Function Calling, Prompt Templates, Memory/Chat History, and Planners/Agents onto the Agent Framework's agents/workflows model.

Must-Have Skills:

• Semantic Kernel + Microsoft Agent Framework — hands‑on experience refactoring SK plugins, planners, function calling, and memory to Agent Framework agents/workflows (this is the critical, hardest‑to‑source skill)
• Python / Django — to rebuild the orchestration layer without breaking existing API contracts, RBAC, and permissions
• Azure AI Services / RAG — Azure AI Search (hybrid retrieval, semantic ranking), Azure OpenAI (inference + embeddings), preserving retrieval, reranking, prompt assembly, and citation paths

High-Priority Skills

• Model Context Protocol (MCP) — for tool/connector integration and the Intelligence Layer channel
• OpenShift / containerization — to deploy migrated microservices on OCP
• Observability & Evaluation — Arize Phoenix + OpenTelemetry instrumentation, agent/agentic‑system registration, and RAG quality/eval validation to confirm parity
• GitHub / CI-CD / DevSecOps — PR‑based workflows and vulnerability remediation

Medium-Priority Skills

• Redis Enterprise (caching/session state) and Azure APIM (gateway, rate limiting)

The single most differentiated requirement is combined hands‑on Semantic Kernel and Microsoft Agent Framework experience paired with Python/Django, since that is the exact intersection where the refactor happens. The remaining skills (Azure AI Search, RAG, OCP, observability) align with what the team already runs today.