AI ML Lead

At InfoBeans, we believe in making other people’s lives better— through our work and everyday interactions.

AI ML Lead

Indore/Pune

Artificial Intelligence

10\+ years

Role

NA

Location

Indore/Pune

Experience

10\+ years

Key Skills

Artificial Intelligence, Generative AI, Traditional AI

What will your role look like

Design AI solutions using both traditional ML and GenAI approaches based on problem needs

Lead end\-to\-end implementation of ML projects including data preparation, model development, deployment, and monitoring

Build GenAI\-based applications using LLMs, embeddings, and multi\-step reasoning frameworks (e.g., LangChain, LangGraph)

Conduct research and proof\-of\-concepts to evaluate emerging GenAI models and techniques

Guide and mentor junior engineers; review designs, architecture, and code quality

Develop fine\-tuned and instruction\-tuned LLMs using platforms like Hugging Face, OpenAI, Cohere, etc.

Build agentic AI systems involving tool use, memory, and reasoning frameworks

Ensure AI models are robust, explainable, and cost\-efficient in production

Collaborate with stakeholders to understand business goals and translate them into technical solutions

Communicate complex AI concepts to both technical and non\-technical audiences.

This is a strategic role where you will play a key part in shaping our AI offerings, guiding teams, and solving complex problems using both modern and established AI techniques.

Why you will love this role

Besides a competitive package, an open workspace full of smart and pragmatic team members, with ever\-growing opportunities for professional and personal growth

Be a part of a learning culture where teamwork and collaboration are encouraged, diversity is valued and excellence, compassion, openness and ownership is rewarded.

We would like you to bring along

Experienced AI/ML Engineer with a strong background in both traditional Machine Learning and Generative AI.

Experience in classical AI/ML and 2–3 years in GenAI, total 10 years of experience with the ability to design, research, and build scalable AI solutions across domains.

6–7 years in traditional ML/DL, including regression, classification, clustering, NLP, time series forecasting, and deep learning techniques such as feedforward networks, CNNs, RNNs, LSTMs, GRUs, and attention\-based models for structured, unstructured, and sequential data

2–3 years in Generative AI (LLMs, embeddings, RAG, fine\-tuning, prompt engineering)

Experience with Python, scikit\-learn, TensorFlow, PyTorch, and Hugging Face Transformers

Understanding of ML lifecycle: data wrangling, feature engineering, model training, evaluation, and deployment

Deep experience with LLMs such as GPT, LLaMA, Mistral, Claude, or Gemini and tools like LangChain, LangGraph, and LLamaIndex

Experience building agentic AI solutions (e.g., AutoGPT, ReAct, CrewAI, BabyAGI)

Proficiency with vector databases (FAISS, Pinecone, Weaviate, Chroma) and embedding models (OpenAI, Cohere, BGE, etc.)

Excellent communication and presentation skills

Strong research mindset with the ability to quickly learn and adapt to new GenAI technologies

Ability to assess trade\-offs between classic and generative AI techniques and recommend the best\-fit approach