Ml & GenAI Platform Engineer

Nisum is a leading global digital commerce firm headquartered in California, with services spanning digital strategy and transformation, insights and analytics, blockchain, business agility, and custom software development. Founded in 2000 with the customer-centric motto Building Success Together, Nisum has grown to over 1,800 professionals across the United States, Chile, Colombia, India, Pakistan, and Canada. A preferred advisor to leading Fortune 500 brands, Nisum enables clients to achieve business growth by building the advanced technology needed to reach customers through immersive digital and physical experiences.

Job Description:

What You'll Do:

• 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 operationalise agentic AI workflows with tool use, leveraging 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 in collaboration with data engineering, application teams, and stakeholders.
• Drive adoption of MLOps / LLM Ops practices, including CI/CD automation, versioning, testing, and lifecycle management.
• Ensure security, compliance, reliability, and cost optimisation of AI services deployed at scale.

What You Know:

• 5-9 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 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, RESTRole & responsibilities