ML & GenAI Platform Engineer

What you know

* 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 using 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 with data engineering, application teams, and business 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.

Important attributes for this role

* Strong ownership mindset and platform thinking

* Ability to lead AI platform delivery from concept to production

* Clear communication and ability to translate AI concepts to business stakeholders

* Strong decision-making in architecture and platform design

* Enterprise mindset for reliability, security, and governance

What you'll do

* 8–10 years of experience in ML Engineering, AI Platform Engineering, or Cloud AI Deployment roles.

* Strong proficiency in Python with experience building production-grade AI/ML services.

* Proven experience deploying and supporting GenAI applications in real-world enterprise environments.

* Hands-on experience with RAG systems, embeddings, vector search, and retrieval pipelines.

* Experience with orchestration frameworks including 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).

* Exposure to vector databases such as Pinecone, Weaviate, FAISS, or Azure Cognitive Search.

* Experience deploying agentic AI systems with tool integrations in production.

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

* Familiarity with enterprise governance frameworks for Responsible AI.

Education

* Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (required).

* Master’s degree is a plus.

Compensation

$150-$160K/ PA