Meaningful work,
meaningful life

Key Responsibilities

• Design, develop, and optimize Machine Learning & Deep Learning models using Python and libraries such as TensorFlow, PyTorch, and Scikit-learn

• Work with Large Language Models (e.g., GPT, BERT, T5) to solve NLP tasks such as, semantic search, summarization, chatbots, conversational agents, and document intelligence.

• Lead the development of scalable AI solution including data preprocessing, embedding generation, vector search, and prompt orchestration.

• Build and manage vector databases and metadata stores to support high-performance semantic retrieval and contextual memory.

• Implement caching, queuing, and background processing systems to ensure performance and reliability at scale.

• Conduct independent R&D to implement cutting-edge AI methodologies, evaluate open-source innovations, and prototype experimental solutions

• Apply predictive analytics and statistical techniques to mine actionable insights from structured and unstructured data.

• Build and maintain robust data pipelines and infrastructure for end-to-end ML model training, testing, and deployment

• Collaborate with cross-functional teams to integrate AI solutions into business processes

• Contribute to the MLOps lifecycle, including model versioning, CI/CD, performance monitoring, retraining strategies, and deployment automation

• Stay updated with the latest developments in AI/ML by reading academic papers, and experimenting with novel tools or frameworks

Required Skills & Qualifications

• Proficient in Python, with hands-on experience in key ML libraries: TensorFlow, PyTorch, Scikit-learn, and HuggingFace Transformers

• Strong understanding of machine learning fundamentals, deep learning architectures (CNNs, RNNs, transformers), and statistical modeling

• Practical experience working with and fine-tuning LLMs and foundation models

• Deep understanding of vector search, embeddings, and semantic retrieval techniques.

• Expertise in predictive modeling, including regression, classification, time series, clustering, and anomaly detection

• Comfortable working with large-scale datasets using Pandas, NumPy, SciPy etc.

• Experience with cloud platforms (AWS, GCP, or Azure) for training and deployment is a plus

Preferred Qualifications

• Master’s or Ph.D. in Computer Science, Machine Learning, Data Science, or related technical discipline.

• Experience with MLOps tools and workflows (e.g., Docker, Kubernetes, MLflow, SageMaker, Vertex AI).

• Ability to build and expose APIs for models using FastAPI, Flask, or similar frameworks.

• Familiarity with data visualization (Matplotlib, Seaborn) and dashboarding (Plotly) tools or equivalent

• Working knowledge of version control, experiment tracking, and team collaboration