ML & GenAI Platform Engineer

Role Overview

• We are seeking an experienced ML & Generative AI Platform Engineer with 8 10 years of industry experience, specializing in deploying, scaling, and operating AI/ML and GenAI systems in cloud environments.
• This role is focused on production-grade implementation of ML and LLM-powered applications, Retrieval-Augmented Generation (RAG) pipelines, and agentic AI workflows.
• The ideal candidate has strong Python engineering skills, deep understanding of AI infrastructure, and hands-on experience delivering enterprise AI systems end-to-end.

Key Responsibilities

• Deploy, scale, and operate ML and Generative AI systems in cloud-based production environments (Azure preferred).
• Build and manage enterprise-grade RAG applications using embeddings, vector search, and retrieval pipelines.
• Implement and operationalize agentic AI workflows with tool use, leveraging frameworks such as LangChain and LangGraph.
• Develop reusable infrastructure and orchestration for GenAI systems using Model Context Protocol (MCP) and AI Development Kit (ADK).
• Design and implement model and agent serving architectures, including APIs, batch inference, and real-time workflows.
• Establish best practices for observability, monitoring, evaluation, and governance of GenAI pipelines in production.
• Integrate AI solutions into business workflows in collaboration with data engineering, application teams, and stakeholders.
• Drive adoption of MLOps / LLMOps practices, including CI/CD automation, versioning, testing, and lifecycle management.
• Ensure security, compliance, reliability, and cost optimization of AI services deployed at scale.

Required Qualifications

• 8 10 years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment roles.
• Strong proficiency in Python, with experience building production-ready AI/ML services and workflows.
• Proven experience deploying and supporting GenAI applications in real-world enterprise environments.
• Experience with orchestration frameworks including but not limited to LangChain, LangGraph, and LangSmith.
• Strong knowledge of model serving, inference pipelines, monitoring, and observability for AI systems.
• Experience working with cloud AI ecosystems (Azure AI, Azure ML, Databricks preferred).
• Familiarity with containerization and deployment tools (Docker, Kubernetes, REST APIs).

Preferred Qualifications

• Experience with Azure Databricks, Azure ML, Data Lake, Synapse, or related Azure services.
• Exposure to vector databases such as Pinecone, Weaviate, FAISS, or Azure Cognitive Search.
• Experience deploying agentic AI systems with tool integrations in production.
• Familiarity with enterprise governance frameworks for Responsible AI.

Strong understanding of CI/CD pipelines and DevOps practices for AI platforms