Machine Learning Engineer

Machine Learning Engineer – Generative AI & Agentic AI

Experience: 6–8 Years

Location: Gurugram or Noida

Employment Type: Contract

About the Role

We are looking for an experienced Machine Learning Engineer with strong expertise in

Python, Machine Learning, Generative AI, Agentic AI, and Microsoft Azure.

The ideal candidate will have hands-on experience designing, developing, deploying, and optimizing machine learning models and AI agents. You will work on

scalable ML pipelines, LLM-powered applications, agentic AI workflows, and production-grade AI systems using Microsoft Azure's Data and AI ecosystem.

This role is ideal for an engineer who enjoys working at the intersection of

Machine Learning, Generative AI, LLMs, and Agentic AI and is comfortable rapidly prototyping solutions and taking them through production.

Key Responsibilities

• Design, develop, and maintain scalable machine learning pipelines and integrate them with

Azure Machine Learning.

• Design and implement agentic AI workflows and intelligent AI agents using LLMs, planning, reasoning, tool calling, and decision-making frameworks.
• Build, deploy, optimize, and monitor machine learning models using TensorFlow, PyTorch, or equivalent frameworks.
• Develop secure, scalable, reliable, and production-ready AI agent architectures for enterprise and cross-platform applications.
• Work extensively with the Microsoft Azure AI and Data ecosystem, including:
• Azure Machine Learning
• Azure Databricks
• Microsoft Fabric
• Microsoft AI Foundry
• Build and experiment with Generative AI applications, LLMs, RAG solutions, and agentic workflows.
• Implement AI safety, guardrails, evaluation, observability, and monitoring mechanisms for AI agents and LLM applications.
• Improve agent performance using feedback loops, reinforcement learning techniques, user interaction analysis, and continuous evaluation.
• Design and implement model-serving and deployment architectures for production AI workloads.
• Collaborate with backend, frontend, data engineering, ML engineering, and research teams to deliver end-to-end AI solutions.
• Conduct experiments, benchmarks, and performance evaluations to improve agent

accuracy, reliability, adaptability, reasoning, and conversational quality.

• Rapidly prototype, test, and iterate on AI solutions in a fast-paced, startup-style environment.
• Work closely with senior engineers and researchers to transform innovative AI concepts into

production-grade systems.

• Contribute to best practices around MLOps, model lifecycle management, AI governance, and responsible AI.

Required Qualifications

• 6–8 years of professional experience in Machine Learning, Artificial Intelligence, or ML Engineering.
• Strong programming skills in Python.
• Hands-on experience with TensorFlow, PyTorch, or equivalent machine learning frameworks.
• Proven experience building and deploying machine learning models and AI/LLM-based applications.
• Strong hands-on experience with Generative AI, LLMs, and Agentic AI.
• Strong expertise in Microsoft Azure and Azure AI/Data services.
• Hands-on experience with:
• Azure Machine Learning
• Azure Databricks
• Microsoft Fabric
• Microsoft AI Foundry
• Experience developing scalable ML pipelines and production AI systems.
• Understanding of AI agent orchestration, tool calling, planning, evaluation, safety, and guardrails.
• Experience with model deployment, serving, monitoring, and productionization.
• Strong understanding of machine learning concepts, model evaluation, experimentation, and optimization.
• Excellent problem-solving and analytical skills.
• Ability to work effectively in a fast-paced, collaborative engineering environment.

Good to Have

• Experience with reinforcement learning, agent feedback mechanisms, or preference-based optimization.
• Experience developing AI solutions for enterprise, security, or mission-critical applications.
• Strong knowledge of RAG, vector databases, embeddings, prompt engineering, and LLM optimization.
• Experience with LLM evaluation frameworks and AI observability.
• Experience implementing AI safety, responsible AI, and guardrail mechanisms.
• Knowledge of MLOps, CI/CD, model lifecycle management, and infrastructure automation.
• Experience developing backend services and APIs for AI applications.
• Familiarity with frontend integration and end-to-end AI application development.
• Experience with distributed computing and large-scale data processing.

Key Technical Skills

Must Have

Python Machine Learning Azure ML Generative AI LLMs Agentic AI TensorFlow/PyTorch Azure Databricks Microsoft Fabric Microsoft AI Foundry ML Pipelines Model Deployment AI Agent Deployment

Good to Have

Reinforcement Learning RAG Prompt Engineering Vector Databases LLM Evaluation AI Safety & Guardrails MLOps AI Observability Model Optimization APIs

What We're Looking For

We are looking for someone who can go beyond experimentation and build AI systems that work reliably in production. The successful candidate should be comfortable moving between ML modeling, LLM experimentation, agent architecture, Azure services, deployment, and performance optimization.

You should be a hands-on engineer and problem solver who enjoys experimenting with emerging AI technologies while maintaining strong engineering practices around scalability, security, reliability, and responsible AI.