GenAi Lead Developer

Job Description

Primary Role Primarily responsible for working as Tech Lead i.e. leading the team and mentoring it technically. Provide hands-on technical leadership in the design, development, and deployment of generative AI models and systems. Serve as the technical point of contact for cross-functional teams, ensuring seamless collaboration between product, engineering, and data science teams. Responsibilities & KPIs Guiding the team technically and helping them solve the technical problems, throughout the project. Responsible for defining project goals, timelines, and deliverables in collaboration with stakeholders. Ensure scalability, robustness, and cost-efficiency of AI solutions. Conduct code reviews, provide constructive feedback, and ensure adherence to best practices in AI development. Build and maintain workflows to manage multiple POCs and project workloads efficiently. Collaborate with data scientists and domain experts to ensure model accuracy and relevance. Architect end-to-end Generative AI pipelines for model training, evaluation, and deployment using state-of-the-art tools and techniques Optimize AI models for speed, accuracy, and cost-efficiency. Ensure compliance with security and ethical standards. Address challenges like data drift and model degradation Proactively introduce innovative generative AI methodologies to address complex business challenges. Foster a culture of experimentation and continuous learning within the team. Ensure alignment between business goals and technical implementations. Desired Skills B.E./B.Tech degree in Computer Science, Information Science, or a related field from a leading institute preferred. Good leadership and people management skills. Excellent communication skills for translating technical concepts to non-technical stakeholders. Hands-on experience with Python Hands-on experience in machine learning, natural language processing, deep learning, and Generative AI technologies like LLM, RAG, Multi Modal architecture. Familiarity with frameworks like TensorFlow, PyTorch, Keras or Hugging Face. Familiarity with MLOps and CI/CD pipelines for AI workflows. Knowledge of cloud platforms (AWS, Azure, or GCP) for deploying AI solutions. Familiarity with current AI trends and open source technologies. Strong problem-solving and analytical skills. Ability to work on multiple projects simultaneously and manage priorities effectively. Exposure to financial services domains is a plus. Basic understanding of UI Frameworks (e.g. Angular) is a plus.