Job
Description
Role & responsibilities Lead End-to-End ML Projects: Own the full lifecycle of ML solutions from problem scoping, data exploration, model development, deployment, and post-deployment monitoring. Own the Gen AI Strategy: Define and drive the technical vision and execution strategy for Gen AI use cases across the organization. Architect Scalable Solutions: Design robust ML systems and pipelines that integrate seamlessly into production environments. Team Leadership: Mentor and guide a team of ML engineers and data scientists, fostering a culture of technical excellence, innovation, and continuous learning. Modeling & Research: Stay up to date with the latest advancements in ML/AI and guide experimentation with novel algorithms (e.g., deep learning, NLP, GenAI, reinforcement learning). Build RAG & Prompt Engineering Pipelines: Design scalable architectures for RAG, vector search, prompt tuning, and prompt chaining with tools like LangChain, LlamaIndex, and FAISS. Stakeholder Collaboration: Work closely with product managers, engineers, and domain experts to translate business problems into technical solutions. Operationalize ML: Partner with MLOps/DevOps teams to deploy, monitor, and maintain ML models in production, ensuring reliability and scalability. Preferred candidate profile Bachelors or Master’s degree in Computer Science, Machine Learning, Data Science, or related field (PhD is a plus). 8+ years of hands-on experience in machine learning, with at least 1–2 years in a leadership or mentorship role. Proficiency in Python and ML libraries such as scikit-learn, TensorFlow, PyTorch, HuggingFace Transformers, etc. Strong understanding of statistics, optimization, and model evaluation techniques. Experience with cloud platforms (AWS, GCP, or Azure) and tools like Docker, Kubernetes, MLflow, or Kubeflow. Proficiency in Python and Gen AI frameworks such as LangChain, Crew AI, or similar. Proven track record of deploying ML models into production at scale. Excellent communication and leadership skills.
