ML & GenAI Platform Engineer
Nisum
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San Jose, CA
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Entry level
Posted March 13, 2026
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Responsibilities
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 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
Not Met Priorities
What still needs stronger evidence
Requirements
- 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
- 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.
Preferred Skills
- Experience with orchestration frameworks including LangChain, LangGraph, and LangSmith.
- Experience working with cloud AI ecosystems (Azure AI, Azure ML, Databricks preferred).
- Exposure to vector databases such as Pinecone, Weaviate, FAISS, or Azure Cognitive Search.
- Strong understanding of CI/CD pipelines and DevOps practices for AI platforms.
- Familiarity with enterprise governance frameworks for Responsible AI.
Education
- (Not required) – Education
- (Required) – Bachelor’s degree in Computer Science, Engineering, Data Science, or related field (required).
- (Not required) – Master’s degree is a plus.
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 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
What You Know
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.
Benefits
In addition to competitive salaries and benefits packages, Nisum US offers its employees some unique and fun extras:
Professional Development - We offer in-house technical training and professional learning programs aimed at developing skills across a broad spectrum of topics such as technology, leadership, role-based training, and process expertise. We also offer an annual stipend for employees to attend external courses in order to maintain professional certifications
Health & Wellness Benefits - We believe that your health and welfare are important, and we strive to ensure that you have affordable options available to you, including some plans that are subsidized for employees and their families up to 90%. We also have dental and vision plans in the US where Nisum pays 100% of premiums for employees
Volunteerism Pay - We believe in giving back and in the US, our employees are eligible for up to 40 hours of paid time off each year to volunteer towards the causes that they are most passionate about. This is in addition to personal PTO and paid holidays
Additional Benefits - We offer all the other important benefits to keep employees and their families healthy and financially secure, such as 401(k) retirement savings with a company match, pre-tax parking and transit programs, disability insurance, and Basic Life/AD&D, alongside exclusive employee discounts on a wide variety of products and services.
Compensation Band
$150K - $160K per annum
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
What You Know
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.
Benefits
In addition to competitive salaries and benefits packages, Nisum US offers its employees some unique and fun extras:
Professional Development - We offer in-house technical training and professional learning programs aimed at developing skills across a broad spectrum of topics such as technology, leadership, role-based training, and process expertise. We also offer an annual stipend for employees to attend external courses in order to maintain professional certifications
Health & Wellness Benefits - We believe that your health and welfare are important, and we strive to ensure that you have affordable options available to you, including some plans that are subsidized for employees and their families up to 90%. We also have dental and vision plans in the US where Nisum pays 100% of premiums for employees
Volunteerism Pay - We believe in giving back and in the US, our employees are eligible for up to 40 hours of paid time off each year to volunteer towards the causes that they are most passionate about. This is in addition to personal PTO and paid holidays
Additional Benefits - We offer all the other important benefits to keep employees and their families healthy and financially secure, such as 401(k) retirement savings with a company match, pre-tax parking and transit programs, disability insurance, and Basic Life/AD&D, alongside exclusive employee discounts on a wide variety of products and services.
Compensation Band
$150K - $160K per annum