AI ML Lead @ Infobeans

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 23 years in GenAI, total 10 years of experience with the ability to design, research, and build scalable AI solutions across domains.
• 67 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
• 23 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