ML & GenAI Platform Engineer

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 using frameworks such as Lang Chain and Lang Graph.
• 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.

• 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 Lang Chain and Lang Graph.
• 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.

• 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

• 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 Lang Chain, Lang Graph, and Lang Smith.
• 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 Dev Ops practices for AI platforms.
• Familiarity with enterprise governance frameworks for Responsible AI.

• Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (required).
• Master’s degree is a plus.

$150-$160K/ PA