Design and implement production-grade RAG architectures using Azure AI Foundry, Azure OpenAI, and Azure AI Search (hybrid search, semantic ranking, re-ranking, metadata filtering).

Define and implement evaluation frameworks for RAG and agents (groundedness, relevance, safety, latency, cost) and drive continuous improvement.

Design and build ML models and pipelines (training, validation, deployment, monitoring) using Azure Machine Learning or equivalent.

Apply responsible AI practices: content safety, PII handling, guardrails, access control, and auditability.

Engage with client stakeholders to understand requirements, estimate effort, and present technical solutions.

6 8 years of total experience in software / data / AI engineering, with at least 2 3 years delivering Generative AI solutions in production.

Proven experience designing and shipping RAG systems at scale, including advanced retrieval strategies and evaluation.

Solid AI/ML background: model development, hyperparameter tuning, evaluation, deployment; experience with PyTorch / Tensor Flow / scikit-learn; NLP / transformer fundamentals.

Strong Azure experience: Azure OpenAI, AI Search, Azure ML, Functions, Container Apps / AKS, Cosmos DB, Key Vault, API Management, Application Insights.

Experience mentoring junior engineers and leading small delivery teams.

Azure certifications (AI-102, DP-100, AZ-305).





Experience with Model Context Protocol (MCP) and enterprise tool integrations.

Experience with Infrastructure as Code (Bicep / Terraform)."

Experience with Microsoft Fabric, Databricks, or Synapse for data engineering.

Experience with fine-tuning (LoRA / PEFT), model distillation, or small language models.

Positive to Have

Hands-on experience with MLOps / LLMOps tooling and CI/CD (Azure Dev Ops or Git Hub Actions).

Expert-level Python; experience building scalable APIs and microservices.

Proven experience building AI agents (tool calling, planning, multi-agent orchestration) using Azure AI Foundry and at least one agent framework.

Strong, hands-on expertise in Azure AI Foundry, including model deployments, agents, evaluations, tracing, and content safety.

Required Skills & Experience

Lead technical design discussions, review code and architecture, and mentor engineers on the team.

Implement LLMOps / MLOps practices: CI/CD, model and prompt versioning, observability, and automated testing.

Optimize LLM solutions for cost, latency, and accuracy: prompt optimization, caching, model selection, fine-tuning where appropriate.

Architect and build single- and multi-agent systems using Azure AI Foundry Agent Service, Semantic Kernel, Lang Graph, or Auto Gen, including tool integration, memory, and orchestration.

📌 Azure AI (India)
🏢 TRIGENT SOFTWARE PRIVATE
📍 India