Tech Lead - Data Scientist

• Lead the design, development, and optimization of end-to-end ML and NLP systems in production environments.
• Collaborate on the deployment of machine learning models using ML-Ops/LLM-Ops pipelines on AWS or other cloud platforms.
• Develop Retrieval-Augmented Generation (RAG) systems for complex NLP tasks, ensuring scalability and low-latency processing.
• Architect solutions for the integration and scaling of ML models in distributed environments using cloud technologies such as AWS, Azure, or Google Cloud.
• Maintain and enhance large-scale machine learning models in production, ensuring high availability and performance.
• Drive the automation of model retraining, monitoring, and validation workflows within ML-Ops pipelines.
• Work with cross-functional teams to create optimized, robust, and scalable machine learning solutions that directly impact the business.

Technical Requirements

• 4+ years of hands-on experience in Machine Learning (ML), Natural Language Processing (NLP), and working with Large Language Models (LLMs).
• Proficiency in Python for ML development and integration into production systems.
• Strong experience in deploying ML models using AWS (or other cloud platforms) with a focus on ML-Ops practices for continuous integration, deployment, and monitoring.
• Experience in LLM-Ops and the operationalization of large language models in production environments.
• Proficiency in developing RAGs (Retrieval-Augmented Generation) systems or similar knowledge-enhanced retrieval frameworks.
• Strong foundation in software development best practices for AI/ML, including testing, optimization, and performance tuning.
• Familiarity with cloud services and tools like S3, Lambda, Kubernetes, and Docker for scalable deployments.

Preferred Qualifications

• Hands-on experience with Computer Vision or Reinforcement Learning techniques is a plus.
• Familiarity with database management using Oracle or PL/SQL.
• Experience with Agile development processes and tools such as Jira and Confluence.
• Ability to manage and prioritize multiple projects in a fast-paced, agile environment with a focus on high-quality delivery.

Must-Have Qualifications

• Bachelor's or Master's degree in computer science, Data Science, or related fields from a reputed institution (IIT, DCE, NIT, or equivalent).
• Proven experience in the full ML lifecycle, from research to production, with a focus on performance, scalability, and robustness.
• Strong analytical and problem-solving skills, with a creative and entrepreneurial mindset.