Senior Applied Scientist, GenAI & ML Systems

TraceLink; Pune District; On-site; INR 1,500,000 - 2,500,000; Full time

Job summary

A leading technology company in Pune is seeking a Senior Applied Scientist to lead the design and implementation of advanced GenAI and ML systems for supply chain platforms. You will architect agentic AI capabilities, focusing on multi-agent systems and their operational reliability. Candidates should have at least 6 years of SaaS experience, robust Python skills, and a proven record with complex AI systems. The role demands leadership in technical strategy and real-time data processing, along with collaboration across multiple teams.

Qualifications

• 6+ years experience with SaaS applications on AWS, GCP, or Azure.
• 2+ years experience in designing autonomous agentic systems.
• Proven experience with multi-agent AI systems in production.

Responsibilities

• Architect and deliver agentic AI capabilities for supply chain workflows.
• Design non-deterministic multi-agent systems and their coordination.
• Implement AI system monitoring and feedback loops.

Skills

Cloud-native SaaS development

AI-driven automation

Python

Java

Reinforcement learning techniques

Agent frameworks

Job description

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:
• 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.
• Familiarity with LLM orchestration patterns (tool calling, function routing, memory management, multi-step planning, agent supervision).
• Experience deploying AI solutions in regulated or enterprise environments with strong governance and security expectations.

Who You Are

• A hands-on technical leader who can move between architecture and implementation seamlessly.
• Comfortable working in uncertainty, designing systems where behavior can be probabilistic and emergent.
• Passionate about building intelligent automation that is measurable, safe, explainable, and scalable.
• Strong communicator who can align stakeholders and drive execution across teams.