What are the responsibilities and job description for the ML & GenAI Platform Engineer position at Nisum?
What you know
$150-$160K/ PA
- 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.
- 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 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.
- Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (required).
- Master’s degree is a plus.
$150-$160K/ PA
Salary : $150,000 - $160,000