Ai Lead

11 hours ago

Bangalore Karnataka, India Weekday AI Full time

This role is for one of the Weekday s clients Salary range Rs 2000000 - Rs 3000000 ie INR 20-30 LPA Min Experience 6 years Location Bengaluru JobType full-time We are seeking an experienced AI Lead to architect design and deliver scalable Generative AI GenAI solutions that drive enterprise innovation This role involves leading the development of advanced AI workflows using cutting-edge technologies such as Temporal pgvector and Apache AGE while ensuring reliability performance and compliance across AI systems Requirements Key Responsibilities Architect and design scalable GenAI solutions leveraging Temporal for workflow orchestration and seamless system integration Develop and optimize retrieval-augmented generation RAG pipelines using pgvector for similarity search and Apache AGE for graph-based reasoning Collaborate cross-functionally with data scientists ML engineers and product teams to deliver impactful AI-driven business solutions Define and implement best practices reusable frameworks and architectural patterns to ensure performance scalability and maintainability of GenAI applications Evaluate and integrate emerging GenAI models orchestration techniques and AI services to accelerate adoption across enterprise use cases Uphold data privacy security and compliance throughout AI workflows deployments and integrations Required Skills Experience 6-12 years of total experience with a strong balance of technical expertise and business understanding Proven experience in Generative AI architecture and applied machine learning workflows Proficiency in Temporal for orchestrating distributed and complex workflows Hands-on experience with vector databases and embeddings particularly pgvector Practical knowledge of graph databases ideally Apache AGE for knowledge graph and reasoning applications Strong programming background in Python with solid software engineering principles Deep understanding of LLM fine-tuning RAG pipelines prompt engineering and scalable model deployment strategies

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