ML Engineer IV

About Groww

Groww is India’s fastest growing investment platform. Our long term vision to become the trusted money partner of ~100M Indians with core products in investment & banking. Groww was founded in 2016 by alums from Flipkart, ICICI, IITB, IITD, BITs Pilani and is backed by marquee investors like Tiger Global, Sequoia Capital, Ribbit Capital, and Y Combinator.

OUR VALUES

We take pride in our values and hold ourselves to a high standard on:

• Radical customer centricity

• Simplicity in our products and personalities

• Transparency on everything

• Long term thinking

• Ownership driven culture

Key Responsibilities:

-

- Develop and optimize machine learning models for production deployment

- Lead the implementation of Generative AI applications, including conversational systems and personalization features

- Collaborate with cross-functional teams to integrate AI solutions into existing products

- Monitor and improve the performance of ML systems in production

- Mentor junior ML engineers and promote best practices in AI/ML development

- Participate in technical design discussions and architectural decisions

- Work with stakeholders to translate business requirements into technical solutions

Required Skills and Experience:

- 8-10 years of hands-on machine learning experience, with focus on production deployment

- Strong expertise in Generative AI, NLP, and conversational AI technologies

- Experience building and deploying AI-powered applications at scale

- Proficient in Python and ML frameworks (TensorFlow, PyTorch, Hugging Face)

- Experience with cloud platforms (AWS, GCP, or Azure) for AI workloads

- Strong understanding of ML pipelines, feature engineering, and MLOps

- Proven track record of delivering measurable business impact through AI solutions

- Excellent problem-solving and communication skills

Preferred Qualifications:

- Master's degree in Computer Science, Machine Learning, or related field

- Experience in financial services or fintech domain

- Knowledge of recommendation systems and personalization techniques

- Contributions to open-source ML projects

- Experience with MLOps tools and practices