Senior Machine Learning Engineer / Generative AI Engineer

Nisum; Hyderabad, Pune District; Sur place; INR 4 000 000 - 7 000 000; Plein temps

Résumé du poste

Nisum is hiring an experienced Machine Learning Engineer/Generative AI Engineer to design, deploy, and operate enterprise-scale AI solutions in cloud environments. You will work with cross-functional teams to build AI-powered applications, implement AI workflows, and establish scalable platforms delivering measurable business value.

The role emphasizes RAG-based applications, LangChain/LangGraph orchestration, MCP/ADK tooling, and strong emphasis on MLOps, observability, and governance in

Qualifications

• 8+ years of experience in Machine Learning Engineering or related fields.
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Responsabilités

• Deploy, scale, and manage ML/G AI solutions in cloud environments (Azure preferred).
• Design and develop enterprise-grade RAG applications using embeddings and vector DBs.
• Build AI workflows and orchestration with LangChain/LangGraph.
• Develop model-serving architectures (REST, batch, real-time).
• Establish observability, monitoring, governance, and cost optimization for AI systems.
• Collaborate with data engineering and business teams to integrate AI into workflows.
• Drive MLOps/LLMOps practices including CI/CD, versioning, testing, and deployment.
• Ensure security, reliability, and scalability of AI services in production.
• Contribute to architectural decisions and ongoing AI platform improvements.

Connaissances

Python

LLMs

Generative AI

MLOps

Azure AI

LangChain

LangGraph

Kubernetes

Docker

REST APIs

Vector databases

Formation

Bachelor's degree in CS/Engineering

Outils

Databricks

Azure ML

Docker

Kubernetes

Description du poste

Machine Learning Engineer / Generative AI Engineer

Experience: 8+ Years
Location: India
Employment Type: Full-Time

About the Role

We are seeking an experienced Machine Learning Engineer / Generative AI Engineer to join our growing AI team. In this role, you will be responsible for designing, deploying, and operating enterprise-scale AI and Generative AI solutions in cloud-based production environments. You will work closely with cross-functional teams to build intelligent applications, implement AI workflows, and establish scalable AI platforms that deliver measurable business value.

Key Responsibilities

• Deploy, scale, and manage Machine Learning and Generative AI solutions in cloud environments, with a preference for Azure-based ecosystems.
• Design and develop enterprise-grade Retrieval-Augmented Generation (RAG) applications using embeddings, vector databases, and retrieval pipelines.
• Build and operationalize Agentic AI workflows with tool integration using frameworks such as LangChain and LangGraph.
• Develop reusable AI infrastructure and orchestration frameworks using Model Context Protocol (MCP) and AI Development Kit (ADK).
• Design and implement model-serving architectures, including REST APIs, batch inference, and real-time AI services.
• Establish best practices for observability, monitoring, evaluation, governance, and performance optimization of AI systems.
• Collaborate with Data Engineering, Application Development, and Business teams to integrate AI solutions into enterprise workflows.
• Drive adoption of MLOps and LLMOps practices, including CI/CD automation, model versioning, testing, deployment, and lifecycle management.
• Ensure security, compliance, reliability, scalability, and cost optimization of AI services deployed in production environments.
• Contribute to architectural decisions and continuously improve AI platform capabilities.

Required Skills & Experience

• 8+ years of experience in Machine Learning Engineering, AI Platform Engineering, MLOps, or related domains.
• Strong proficiency in Python and experience building production-grade AI/ML applications and services.
• Hands-on experience with Generative AI and Large Language Model (LLM) applications in enterprise environments.
• Expertise in Retrieval-Augmented Generation (RAG), vector search, embeddings, and retrieval pipelines.
• Experience with orchestration frameworks such as LangChain, LangGraph, and LangSmith.
• Strong understanding of AI model serving, inference pipelines, monitoring, and observability.
• Experience with Azure AI, Azure Machine Learning, Databricks, or similar cloud AI platforms.
• Hands-on experience with Docker, Kubernetes, REST APIs, and cloud-native deployments.
• Knowledge of MLOps/LLMOps practices, including CI/CD, testing, version control, and deployment automation.
• Strong problem-solving skills and the ability to work in a collaborative, fast-paced environment.

Educational Qualification

• Bachelors Degree in Computer Science, Information Systems, Engineering, Computer Applications, or a related field.