Job
Description
Lead Software Engineer GenAI Development and Exploration Location: India Experience: 510 Years Primary Skillset: Generative AI Services, Machine Learning, Python, Cloud, MLOps, Prompt Engineering About the Role: We are seeking a visionary and hands-on Lead Software Engineer to drive Generative AI development and exploration initiatives within our technology innovation team. This role is ideal for someone passionate about applying GenAI to real-world enterprise challenges and who enjoys building proof-of-concepts, production-ready solutions, and scalable AI platforms. Key Responsibilities: Lead the design and development of Generative AI applications, including LLM-based solutions, document intelligence, summarization, chatbot development, and intelligent automation. Evaluate and integrate GenAI services and APIs (e.g., Vertex AI, OpenAI, Anthropic, HuggingFace, etc.) into existing or new platforms. Build scalable backend services and data pipelines to support AI/ML workloads. Explore and validate use cases across multiple business functions for GenAI application and automation opportunities. Mentor junior engineers on GenAI design patterns, best practices, and engineering discipline. Partner with data scientists, product teams, and business stakeholders to define requirements, build MVPs, and iterate toward enterprise-scale solutions. Champion MLOps practices for GenAI model deployment, monitoring, and lifecycle management. Contribute to technical research, architecture recommendations, and the long-term GenAI roadmap. Qualifications: Bachelor's or Master's degree in Computer Science, AI/ML, Data Science, or related field. 8+ years of experience in software engineering with 23 years working directly with AI/ML models or GenAI solutions. Strong experience with Python, cloud platforms (especially GCP Vertex AI), and ML frameworks. Hands-on experience with LLMs, prompt engineering, fine-tuning, and embeddings. Experience in building and integrating GenAI services in real-time or batch applications. Solid understanding of machine learning lifecycle, data preprocessing, and deployment pipelines. Familiarity with CI/CD pipelines, API development, and cloud-native architectures. Excellent communication, collaboration, and stakeholder management skills.
