Senior Machine Learning Engineer

Sequoia; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

Sequoia in Bengaluru, India is seeking a Senior Machine Learning Engineer with a strong engineering foundation to build, deploy, and scale ML and GenAI solutions in production.

The ideal candidate has 5+ years of experience across the ML lifecycle, from data acquisition to MLOps, deployment, monitoring, and business impact measurement. You will collaborate with product and engineering teams to deliver real-world AI applications.

Qualifications

• 5+ years of experience across the complete ML lifecycle.
• Strong engineering foundation and production-grade ML/GenAI delivery.
• Track record of delivering commercial AI products and GenAI applications.

Responsibilities

• Design, build, deploy end-to-end ML solutions at scale.
• 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.

Tools

Python

SQL

Spark

PyTorch

TensorFlow

Scikit-learn

LLMs

Agentic AI

RAG

OpenAI

Anthropic Claude

Mistral

Gemini

Job description

We are seeking a highly experienced Senior 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:
• Evaluation
• Monitoring and retraining

Generative AI

• Build enterprise-grade GenAI applications using:
• OpenAI
• Anthropic Claude
• Mistral
• Gemini
• Design and implement:
• Agentic AI systems
• Multi-agent frameworks

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:

• Python, SQL, Spark
• ML/DL frameworks (PyTorch, TensorFlow, Scikit-learn)
• LLMs, RAG, Agentic AI