Senior GenAI engineer

About the Role:
We are seeking a highly skilled Lead Data Scientist with 4-8 years of experience to drive innovation in machine learning, deep learning, and generative AI.

The ideal candidate should have expertise in Large Language Models (LLM), Retrieval-Augmented Generation (RAG), AI Agents, and hands-on experience in LangChain or LlamaIndex, and Knowledge Graphs. The role also requires strong technical leadership and experience in AI Agent frameworks and libraries.

Key Responsibilities:
Lead the development and deployment of machine learning models, deep learning frameworks, and AI-driven solutions across the organization.
Work closely with stakeholders to define data-driven strategies and drive innovation using AI and machine learning.
Design and implement robust data pipelines and workflows in collaboration with data engineers and software developers.
Develop and deploy APIs using web frameworks for seamless integration of AI/ML models into production environments.
Mentor and lead a team of data scientists and engineers, providing technical guidance and fostering professional growth.
Leverage LangChain or LlamaIndex to enhance model integration, document management, and data retrieval capabilities.
Lead projects in Generative AI technologies, such as Large Language Models (LLM), Retrieval-Augmented Generation (RAG), and AI agents, to create innovative AI-driven products and services.
Stay updated on the latest AI/ML trends, ensuring that cutting-edge methodologies are adopted across projects.
Collaborate with cross-functional teams to translate business problems into technical solutions and communicate findings effectively to both technical and non-technical stakeholders.

Required Skills and Qualifications:
Experience: 4-8 years of experience in data science and AI/ML, with a strong foundation in machine learning, deep learning, generative AI and data engineering.
Generative AI Expertise: Minimum 2 years of experience with gneretaive AI. Hands-on experience with LLMs, RAG, and AI agents.
AI Agents & Frameworks: Hands-on experience with AI agent frameworks/libraries (e.g., LangGraph, CrewAI, OpenAI's Function Calling, Semantic Kernel, etc.).
Programming: Strong proficiency in Python, with experience using TensorFlow, PyTorch, and Scikit-learn.
LangChain & LlamaIndex: Experience integrating LLMs with structured and unstructured data.
Knowledge Graphs: Expertise in building and utilizing Knowledge Graphs for AI-driven applications.
SQL & NoSQL Databases: Hands-on experience with SQL (PostgreSQL, MySQL, etc.) and NoSQL (MongoDB, Cassandra, etc.) database.
API Development: Experience in developing APIs using Flask or FastAPI or Django.
Cloud & MLOps: Experience working with cloud providers like AWS, GCP, Azure and MLOps best practices.
Excellent communication, leadership, and project management skills.
Strong problem-solving ability with a focus on delivering scalable, impactful solutions.

Preferred Skills:
Experience with Computer Vision applications.
Chain of Thought Reasoning: Familiarity with CoT prompting and reasoning techniques.
Ontology: Understanding of ontologies for knowledge representation in AI systems.
Data Engineering: Experience with ETL pipelines and data engineering workflows.
Familiarity with big data tools like Spark, Hadoop, or distributed computing.