Staff/Senior Applied Scientist, GenAI & ML Systems (Pune)

Company overview: TraceLinks software solutions and Opus Platform help the pharmaceutical industry digitize their supply chain and enable greater compliance, visibility, and decision making. It reduces disruption to the supply of medicines to patients who need them, anywhere in the world. Founded in 2009 with the simple mission of protecting patients, today Tracelink has 8 offices, over 800 employees and more than 1300 customers in over 60 countries around the world. Our expanding product suite continues to protect patients and now also enhances multi-enterprise collaboration through innovative recent applications such as MINT. Tracelink is recognized as an industry leader by Gartner and IDC, and for having a great company culture by Comparably. Senior Applied Scientist, GenAI & ML Systems Location: Pune, India About the Role We are seeking a highly experienced Senior Applied Scientist, GenAI & ML Systems, to lead the design, architecture, and implementation of advanced agentic AI / GenAI systems within our next-generation supply chain platforms. In this role, you will build and evolve complex multi-agent systems capable of reasoning, planning, and executing workflows in dynamic and often non-deterministic environments. You will also be responsible for developing robust approaches to testing, validation, observability, and reliability of AI-driven behavior in production. This role is ideal for a senior technical leader with deep experience in cloud-native SaaS development, AI-driven automation, and modern software engineering practices. Experience in life sciences supply chain or regulated industry ecosystems is a significant advantage. Key Responsibilities - Architect and deliver agentic AI / GenAI capabilities that automate and coordinate complex supply chain workflows at scale. - Design and implement non-deterministic multi-agent systems, including agent coordination, tool execution, planning, memory, and feedback loops. - Own technical strategy for building reliable AI systems, including: - agent evaluation frameworks - simulation-based testing - regression suites for non-deterministic outputs - validation of agent decision-making and outcomes - Build and operationalize advanced knowledge retrieval systems, including RAG pipelines, hybrid retrieval, ranking, and domain-grounding strategies. - Design scalable backend services and system infrastructure using Java and Python, ensuring production-grade performance, security, and observability. - Implement AI system monitoring and feedback loops, including agent trace capture, prompt/tool auditing, and performance metrics. - Fine-tune and optimize small language models (SLMs) for domain performance, cost efficiency, latency, and task specialization. - Apply and experiment with reinforcement learning techniques in NLP / GenAI / agentic workflows, including reward modeling or iterative improvement loops where appropriate. - Collaborate with product, data science, and domain experts to translate supply chain requirements into intelligent automation features. - Guide architecture across distributed services, event-driven systems, and real-time data processing using cloud-native design patterns. - Mentor engineers, influence technical direction, and establish system standards and best practices across teams. Required Qualifications - 6+ years of experience building and operating SaaS applications on AWS, GCP, or Azure (minimum 5 years with AWS). - 2+ years of experience designing and running autonomous agentic systems in supply chain domains (logistics, manufacturing, planning, procurement, or similar). - 6+ years of hands-on Python experience delivering large-scale, production-grade services. - Proven experience building, deploying, and operating complex multi-agent AI / GenAI systems in production, including evaluation and monitoring of non-deterministic behaviors. - Strong experience with context engineering for multi-agent systems, including prompt design, memory and state management, tool grounding, and long-horizon task reliability. - Hands-on experience with one or more agent frameworks (e.g., LangGraph, AutoGen, CrewAI, Semantic Kernel, or equivalent). - Experience building and operating advanced knowledge systems (e.g., RAG, hybrid search, reranking, grounding and citation strategies). - Experience fine-tuning and deploying language models and/or applying RL techniques in GenAI or agentic AI contexts. - Solid understanding of distributed systems, microservices, and production reliability best practices. Preferred Qualifications - Knowledge of the life sciences supply chain, including regulated environments, pharma manufacturing/distribution, or related compliance ecosystems. - Experience with Java and JavaScript/ECMAScript is a plus. - Familiarity with LLM orchestration patterns (tool calling, function routing, memory management, multi-step planning, agent supervision). - Experience deploying AI solutions in .