4.0 - 6.0 years

0 Lacs

bengaluru, karnataka, india

On-site

As a Senior Data Scientist, you will lead the development of scalable GenAI-powered systems, designing intelligent workflows that leverage large language models (LLMs), vector-based retrieval, and multi-agent orchestration frameworks. Youll drive solution architecture, mentor junior engineers, and deliver production-ready applications that integrate deeply with business processes and platforms. Key Responsibilities: Lead the design and deployment of GenAI systems leveraging LLMs, retrieval pipelines, and orchestration frameworks for multi-step task execution Architect and optimize prompt workflows, including chaining, templating, and context control, for high-accuracy and cost-efficient solutions Build and maintain embedding-based retrieval systems using vector databases and context-aware generation techniques (e.g., retrieval-augmented generation) Collaborate with product owners and engineering leads to align solution architecture with business objectives Guide and mentor junior engineers on best practices in prompt design, token optimization, security controls, and observability patterns Define standards for code modularity, response consistency, prompt safety, and testing across LLM-powered applications Maintain strong CI/CD practices using version-controlled workflows and cloud-native deployment pipelines Evaluate emerging GenAI tooling and provide technical recommendations for experimentation and adoption Qualifications 4+ years of experience in AI/ML solution delivery, with a strong focus on GenAI or LLM-integrated systems Expertise in Python (v3.11+) with deep familiarity in LLM APIs, embedding generation, vector-based search, and modular pipeline design Proven experience in building and deploying prompt-driven applications at scale Solid understanding of agent orchestration patterns, multi-agent task flows, and context layering techniques Hands-on experience in cloud-native delivery (preferably Azure), including containerization, CI/CD, and monitoring Show more Show less

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