Lead Machine Learning Engineer

Sequoia; Bengaluru; On-site; INR 3,000,000 - 5,400,000; Full time

Job summary

Sequoia is seeking a highly experienced Lead Machine Learning Engineer in Bengaluru, India. You will design, build, deploy and monitor production-grade ML and GenAI solutions at scale, driving measurable business impact.

The role requires 5+ years across the ML lifecycle, from data acquisition to MLOps, with a track record of delivering commercial AI products using modern LLMs and GenAI frameworks.

Qualifications

• 5+ years of end-to-end ML experience including deployment and monitoring.
• Proven track record delivering commercial AI products.
• Experience with GenAI frameworks and modern LLMs.
• Strong ML lifecycle knowledge from data to production.

Responsibilities

• Design, develop, deploy end-to-end ML solutions at scale.
• Build predictive models, NLP solutions, and deep learning apps.
• Drive data science lifecycle: feature engineering and MLOps.
• Collaborate with product and engineering to deliver impact.
• Drive MLOps practices, governance, and scalability.

Skills

MLOps

Model deployment

LLM optimization

GenAI frameworks

NLP

Deep learning

Data science lifecycle

Collaboration with product

Business impact

Tools

Anthropic Claude

Job description

We are seeking a highly experienced Lead Machine Learning Engineer with a strong engineering

foundation and deep expertise in building, deploying, and scaling Machine Learning and Generative

AI solutions in production environments.

The ideal candidate will have 5+ years of experience across the complete ML lifecycle, from data

acquisition and model development to MLOps, deployment, monitoring, and business impact

measurement. The candidate should have demonstrated success in delivering commercial AI

products and building production-grade AI applications leveraging modern LLMs and Generative AI frameworks.

Key Responsibilities:

• Design, develop, and deploy end-to-end ML solutions at scale.
• Build and optimize predictive models, recommendation systems, NLP solutions, and deep learning applications.
• Drive the complete data science lifecycle:
• Feature engineering
• Generative AI
• Build enterprise-grade GenAI applications using:
• Anthropic Claude
• Design and implement:

Key Responsibilities:

• Design, build, deploy, and monitor production-grade ML solutions
• Develop AI/ML applications using modern ML and GenAI frameworks
• Build and optimize end-to-end ML pipelines
• Collaborate with Product and Engineering teams to deliver business impact
• Drive best practices in MLOps, model governance, and scalability

Preferred Skills:

• MLOps and model deployment

What Sets You Apart:

• Experience optimizing LLMs for production (cost, latency, scaling)
• Track record of maintaining AI systems in production
• Ability to balance innovation with practical business needs
• Experience with HR/people analytics domain