Job Description

What you'll be liable for:

• Big picture: Understand customers’ needs and innovate and use cutting-edge Machine Learning techniques to build data-driven solutions.
• Work on NLP problems across areas such as voice agents, agentic applications, text classification, entity extraction, and summarisation, using LLMs.
• Collaborate with cross-functional teams to integrate/upgrade AI solutions into company’s products and services Optimise existing machine learning models for performance, scalability and efficiency.
• Help implement and evaluate reasoning, planning, and memory modules for agents.
• Build, deploy and own scalable production NLP pipelines.
• Build post-deployment monitoring and continual learning capabilities.
• Propose suitable evaluation metrics and establish benchmarks.
• Keep abreast of SOTA techniques in your area and exchange knowledge with colleagues.
• Desire to learn, implement and apply latest emerging model architectures (like LLMs), inference optimizations, distributed training, using open-source models, etc.

We'll love to explore more about you if you have:

• B.Tech/M.Tech/PhD in computer science or mathematics-related fields from tier-1 engineering institutes with 3+ years of industry experience in Machine Learning and NLP.
• Strong coding skills in Python and Pytorch with familiarity with libraries like Transformers and LangChain/LangGraph.
• Strong practical experience in NLP problems in areas such as text classification, entity tagging, information retrieval, question-answering, natural language generation, clustering, etc.
• Knowledge and hands-on experience with Transformer-based Language Models like BERT, Llama, Qwen, Gemma, DeepSeek, etc.
• In-depth familiarity with LLM training concepts, model inference optimizations, GPUs, etc.
• Experience with ML and Deep Learning model deployments using REST API, Docker, Kubernetes, etc.
• Good problem-solving skills involving data structures and algorithms.
• Knowledge of cloud platforms (AWS/Azure/GCP) and their machine learning services is desirable.
• Knowledge of multimodal models is a plus
• Knowledge of real-time streaming tools/architectures like Kafka and Pub/Sub is a plus.

Bonus points – Past research experience and accepted papers in the field of NLP or Machine Learning
• Contributions to AI side projects or GitHub repos