Machine Learning Engineer

Designation: ML & Generative AI Engineer (Senior)

Location: Gurugram (WFO)

Experience: 6+ Years

Industry: AI Product (B2B Solution)

Overview

We are a fast-growing, technology-driven organization focused on building intelligent, scalable AI solutions that create measurable business impact. Our teams work on cutting-edge machine learning and Generative AI use cases, transforming complex data into production-grade systems used across products and platforms.

This role offers an opportunity to work closely with cross-functional stakeholders, take ownership of AI-driven initiatives, and contribute to the evolution of modern ML and GenAI systems in a real-world production environment.

Key Responsibilities

• Design, build, and deploy end-to-end machine learning and Generative AI pipelines for production use
• Develop and optimize ML models, deep learning systems, and LLM-based solutions
• Implement Generative AI use cases including RAG pipelines, prompt engineering, fine-tuning, and agent-based workflows
• Collaborate with data scientists, backend engineers, and product teams to integrate AI capabilities into existing systems
• Apply MLOps best practices such as model versioning, experiment tracking, CI/CD, and performance monitoring
• Participate in technical design discussions and contribute to AI architecture decisions
• Analyze large datasets to improve model accuracy, efficiency, and scalability
• Stay current with emerging AI/ML tools, frameworks, and methodologies and apply them where relevant
• Clearly communicate technical outcomes, trade-offs, and recommendations to stakeholders.

Required Skills & Qualifications

• Bachelors degree in Computer Science, Machine Learning, Data Science, or a related field
• 6+ years of experience in software engineering, with strong focus on AI/ML development
• Strong proficiency in Python and hands-on experience with ML frameworks such as PyTorch, TensorFlow, scikit-learn
• Practical experience working with Large Language Models (LLMs) including proprietary or open-source models
• Experience building Generative AI solutions such as RAG systems, prompt engineering, or fine-tuning workflows
• Exposure to cloud platforms (AWS, GCP, or Azure) for ML model training and deployment
• Understanding of MLOps practices including model deployment, monitoring, and experimentation
• Strong problem-solving skills and ability to work collaboratively in cross-functional teams