About The Role:
As a Manager – Machine Learning, you will lead the design and implementation of AI solutions using both traditional Machine Learning and Generative AI approaches. You will guide teams in developing scalable AI applications, including LLM-based and agentic AI systems, while ensuring robustness, explainability, and cost-efficiency. This strategic role involves collaborating with stakeholders, conducting research, and shaping AI offerings across domains.

Roles & Responsibilities:
Design AI solutions using traditional ML and Generative AI based on problem requirements.
Lead end-to-end ML projects, including data preparation, model development, deployment, and monitoring.
Build GenAI 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 translate business goals into technical AI solutions.
Communicate complex AI concepts to technical and non-technical audiences.

Required Qualifications:

10+ years of experience in AI/ML, including 6–7 years in traditional ML/DL and 2–3 years in Generative AI.
Strong programming skills in Python and expertise in ML/DL frameworks: scikit-learn, TensorFlow, PyTorch, Hugging Face Transformers.
Deep experience with LLMs (e.g., GPT, LLaMA, Mistral, Claude, Gemini) and tools like LangChain, LangGraph, 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).
Strong understanding of the ML lifecycle: data wrangling, feature engineering, model training, evaluation, and deployment.
Excellent communication, presentation, and mentorship skills.
Ability to assess trade-offs between classic and generative AI techniques and recommend best-fit approaches.

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