ML & GenAI Platform Engineer

Nisum Consulting Private Limited

Job Location:

San Jose, CA - USA

Monthly Salary: Not Disclosed

Posted on: Yesterday

Vacancies: 1 Vacancy

Job Summary

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 youll 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

Bachelors degree in Computer Science Engineering Data Science or related field (required).
Masters degree is a plus.

Compensation

$150-$160K/ PA

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 us...

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 youll 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.