Job Summary :

Provides technical leadership and strategic direction for the AI/ML team. Designs and implements AI/ML solutions that meet business needs and drive innovation.

Job Description :

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