Position Type: Permanent - Lead

Mode of Interview: Virtual

Experience: 15 to 22 Years

Work Mode: Hybrid

Skills: GenAI, autonomous agents, advanced NLP, machine learning and hands-on technical expertise

Some of the key responsibilities will include:

Technical Leadership & Strategy:

- Lead the design, incubation, and scaling of AI/ML solutions that solve complex business problems.

- Architect and implement GenAI - powered systems, including Retrieval - Augmented Generation (RAG) pipelines and autonomous agents.

- Evaluate and integrate open - source and proprietary LLMs, optimizing for performance and business value.

Solution Delivery:

- Translate business requirements into robust, scalable AI/ML solutions.

- Collaborate with cross - functional teams to ensure seamless integration of AI capabilities into products and platforms.

- Drive experimentation, rapid prototyping, and iterative development.

Team Development & Mentorship:

- Guide and mentor data scientists, ML engineers, and junior developers.

- Foster a culture of technical excellence, innovation, and continuous learning.

Operational Excellence :

- Partner with CoE leaders to participate in and improve delivery processes, resource allocation, and prioritization frameworks.

- Establish best practices for model development, deployment, monitoring, and governance.

To be eligible for this role you will require :

- Experience : 15 - 22 years

- Strong foundation in statistics, machine learning, and deep learning.

- Proven experience in building and deploying GenAI solutions using frameworks like LangChain, LangGraph, or similar.

- Expertise in Natural Language Processing (NLP), including semantic search, entity recognition, and text generation.

- Hands - on experience with LLMs (e.g., GPT, LLaMA, Claude, Mistral) and fine - tuning/customization techniques.

- Ability to design and implement autonomous AI agents capable of observation, planning, reasoning, and action.

- Proficiency in analyzing large datasets to identify trends, model improvements, and optimization opportunities.

- Familiarity with MLOps/AIOps practices and tools for scalable model deployment and lifecycle management.

- Advanced degree (MSc/PhD) in Computer Science, Data Science, AI/ML, or related field preferred.

- Experience in cloud platforms (Azure, AWS, GCP) and containerization (Docker, Kubernetes) preferred.

- Knowledge of enterprise AI governance, ethical AI, and model interpretability preferred.