AI Lead

3 weeks ago

Bengaluru, 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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