Technical Lead - GenAi

Sigmoid Analytics is a leading Data solutions company backed by Sequoia Capital. We offer best-in-end-to-end data value chain spanning across Data Science, Data Engineering and Data Ops. With data and technology at the core of our solutions, we are solving some of the toughest problems out there. Our culture is modelled around expertise and mutual respect with a team first mindset. You'll work with teams that push the boundaries of what-is-possible and build solutions that energize and inspire.

Offices : New York | Dallas | San Francisco | Lima | Bengaluru The below role is for our Bengaluru office.

About the Role :

We are seeking a highly skilled and motivated AI Engineer to join our dynamic team, specifically for an ATL / TL role. You will be responsible for designing, developing, and deploying AI solutions, with a focus on Natural Language Processing (NLP) and model performance tuning. You will collaborate closely with data scientists, analysts, and other stakeholders to integrate AI capabilities into our products and services. Beyond individual contributions, you will play a key role in guiding and mentoring junior team members, driving technical excellence, and ensuring the scalability and robustness of our machine learning initiatives.

Required Skills :

• Proficiency in Python for NLP and AI model development and performance
• Strong understanding of Natural Language Processing (NLP) principles and
• Solid foundation in Artificial Intelligence (AI) concepts and
• Experience in Model Performance Tuning to optimize and enhance AI / NLP
• Demonstrated experience in scaling machine learning projects from proof-of-concept to
• Proven ability to design and implement scalable machine learning models and
• Strong background in Data Engineering principles, with experience in building robust data pipelines for ML
• Experience with distributed computing frameworks for large-scale data processing and model

Preferred Skills :

Minimum Qualifications :

Skills Required

data engineering , Machine Learning, Distributed Computing, Python