Lead Data & AI Scientist
First seen by Alion on Sep 3, 2026.
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.
Skills
Machine Learning, NLP, Generative AI, Data Science, Data Scientist, LLM, Python, SQL, ETL, AIOps