Data Scientist

About the role:

• We are looking for experienced Data Scientists with strong Python expertise to join our growing AI/ML team. You’ll collaborate with a world-class group of machine learning engineers and scientists working on cutting-edge NLP, document understanding, and enterprise automation use cases.

Key Responsibilities:

• Design, build, and evaluate models for NLP, document extraction, classification, and generative tasks.
• Develop end-to-end ML pipelines from data pre-processing to model inference and monitoring.
• Work on productionizing models including model packaging, API integration, and deployment using Docker/Kubernetes.
• Analyse model behaviour, debug Python code and optimize performance in large-scale environments.
• Translate prototypes into scalable, production-grade ML services, with a focus on reliability and performance.
• Contribute to model and system monitoring, logging, and performance optimization.
• Collaborate with product managers and engineering teams to turn business requirements into ML-driven product features.
• Stay current with research and advancements in transformer-based architectures, LLMs (e.g., GPT, BERT), and generative AI techniques.

Must-Have Qualifications:

• 2–5 years of professional experience in Python, with strong debugging, profiling, and performance optimization skills.
• Solid understanding of python data structures, algorithms, and software engineering best practices in ML development.
• Hands-on experience with NLP and modern ML frameworks like PyTorch, TensorFlow, or Hugging Face Transformers.
• Applied experience with transformer models, LLMs, or generative AI in real-world scenarios.
• Experience with model evaluation, including designing meaningful metrics, tracking model drift, and optimizing performance in production.
• Ability to manage multiple priorities in a fast-paced and collaborative environment.
• B.E./ B.Tech or higher in Computer Science, Engineering, or a related technical field.

Nice-to-Haves:

• Experience building and deploying containerized ML services with Docker and CI/CD pipelines.
• Skilled in designing and consuming RESTful Python APIs (e.g., FastAPI, Flask).
• Experience with cloud services, particularly AWS (S3, SQS, etc.).
• Familiarity with databases such as PostgreSQL and Redis.
• Strong grasp of classical ML algorithms such as Logistic Regression, Random Forests, and XGBoost.
• Ability to choose between heuristic, rule-based, and model-driven solutions pragmatically (e.g., regex vs ML).