Responsibilities
Architect enterprise-grade GenAI systems using modular LLM APIs, agent orchestration frameworks, and embedding pipelines
Design and implement autonomous agent workflows with context management, multi-agent coordination, and task delegation
Optimize performance, latency, and accuracy through experimentation with prompt strategies, retrieval layers, and caching logic
Lead solution reviews, enforce prompt safety and governance, and ensure alignment with security protocols
Collaborate with platform, product, and engineering leads to define reusable patterns and scalable AI capabilities
Guide engineering pods on GenAI design principles, system reliability, and prompt lifecycle management
Build and maintain reusability assets SDKs, templates, shared agent logic to accelerate delivery velocity across teams
Stay up to date with advancements in LLM tooling, orchestration abstractions, and prompt optimization techniques

Required Qualifications
68+ years of experience in AI/ML engineering,



with a solid focus on designing and scaling GenAI applications
Deep proficiency in Python 3.11+ and experience with LLM APIs, vector databases, embedding generation, and agent coordination
Hands-on expertise in architecting agent-based workflows using framework-agnostic orchestration patterns
Proven track record in deploying secure, cost-effective, cloud-native GenAI solutions (preferably in Azure ecosystem)
Solid grasp of CI/CD, containerization, and model monitoring practices

Preferred Qualifications
Exposure to model context protocols (MCP) and autonomous agent-to-agent (A2A) interactions
Contributor to reusable GenAI accelerators, prompt chaining templates, or internal developer tools
Familiarity with governance and observability tools for LLM workflows (e.g., cost tracking, safety controls, token usage analytics)
Ability to simplify and communicate technical decisions to both engineers and non-technical stakeholders

📌 Senior Data Scientist Bengaluru (India)
🏢 Ecolab Global Services
📍 India