Lead Artificial Intelligence Engineer

Ecolab Food Safety & Hygiene Solutions Private Limited; Bengaluru; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

Ecolab Food Safety & Hygiene Solutions Private Limited in Bangalore (Hybrid) seeks a Lead AI Engineer to own GenAI system architecture and delivery, guiding cross-functional pods and setting engineering standards.

You will lead a team, design autonomous agent workflows, optimize performance, and ensure security governance while collaborating with platform, product, and engineering leads to scale AI capabilities.

Qualifications

• 6 to 8+ years in software, AI, or ML with GenAI or agentic AI production delivery.
• Proven experience leading LLM-powered products and operational readiness.
• Strong Python backend skills and API design.
• Hands-on with Azure OpenAI, Azure AI Studio, LangChain, AutoGen, or equivalent platforms.
• Experience with retrieval-augmented generation (RAG) and vector stores (e.g., Pinecone, FAISS).
• Experience deploying cloud-native AI services (Azure Functions, Docker, FastAPI, Azure DevOps).
• Knowledge of observability tools (Application Insights, OpenTelemetry, Datadog).
• Familiarity with MCP/A2A-like context-sharing and distributed agent patterns.

Responsibilities

• Architect enterprise-grade GenAI systems with modular LLM APIs and embedding pipelines.
• Design autonomous agent workflows with context management and multi-agent coordination.
• Optimize performance, latency, and accuracy with prompt strategies and caching.
• Lead reviews, enforce prompt safety and governance, align with security protocols.
• Collaborate with platform, product, and engineering to define reusable AI patterns.
• Guide pods on GenAI design principles, reliability, and lifecycle management.
• Create reusability assets like SDKs, templates, and shared agent logic.
• Stay updated on LLM tooling and orchestration abstractions.

Skills

Team leadership

Cross-functional collaboration

Architectural design

Python programming

GenAI experience

Tools

Azure OpenAI

LangChain

Semantic Kernel

Pinecone

Weaviate

FAISS

Docker

FastAPI

GitHub

Azure DevOps

OpenTelemetry

Application Insights

Datadog

Job description

Lead AI Engineer Lead AI Engineer Bangalore (Hybrid)

ROLE SUMMARY

As a Lead AI Engineer, you will own the architecture and delivery of GenAI-based systems that integrate large language models (LLMs), multi-agent workflows, and embedding-powered retrieval solutions. You will guide cross-functional pods, define engineering standards, and drive innovation through scalable, production-grade intelligent applications. You will lead a team of associates both functionally and admin responsibilities.

KEY 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

• 6 to 8+ years of experience in software, AI, or ML engineering roles, including significant experience designing, delivering, and operating production-grade GenAI or agentic AI applications
• Proven experience leading the technical delivery of LLM-powered products or agent-based solutions, including solution design, engineering guidance, and operational readiness
• Strong technical foundation in Python and modern backend engineering patterns, with practical experience building AI-enabled application services and APIs
• Hands-on experience with Azure OpenAI, Azure AI Studio, Semantic Kernel, LangChain, AutoGen, or equivalent platforms and orchestration frameworks, including real-world use of LLM APIs, prompt workflows, tool calling, and agent coordination
• Strong experience designing and implementing retrieval-augmented generation (RAG) and vector-based patterns using platforms such as Azure AI Search, Pinecone, Weaviate, FAISS, or equivalent
• Experience building and deploying cloud-native AI services using technologies such as Azure Functions, Azure Container Apps, FastAPI, Docker, Azure DevOps, GitHub, or equivalent engineering and deployment platforms
• Solid understanding of CI/CD, containerization, automated testing, and production deployment practices for AI-driven systems
• Practical experience with observability and operational tooling such as Application Insights, OpenTelemetry, Azure Monitor, Datadog, New Relic, or equivalent, including monitoring of reliability, latency, and cost
• Exposure to Model Context Protocol (MCP), agent-to-agent (A2A) interaction patterns, or similar context-sharing and distributed agent communication approaches
• Strong ownership mindset across the full SDLC, including design, build, deployment, support, reliability improvement, and long-term maintainability
• Proven ability to raise engineering quality through code reviews, technical mentoring, design guidance, and reuse of shared patterns and components
• Strong collaboration and communication skills, with the ability to work effectively across engineering, architecture, product, and platform teams

Preferred Qualifications

• Experience leading the design or implementation of agentic AI workflows involving multi-step reasoning, tool orchestration, and reusable orchestration patterns
• Experience with Microsoft AI Foundry, Azure Machine Learning, Azure AI / Copilot Studio, or equivalent platforms used for enterprise AI solution development and experimentation
• Familiarity with enterprise integration and application ecosystems, including AI integration with APIs, workflow platforms, and downstream business systems
• Experience contributing to reusable GenAI accelerators, prompt libraries, orchestration templates, internal AI developer platforms, or engineering toolkits
• Familiarity with AI governance, safety, observability, and cost-management tooling, including token usage analytics, quality evaluation, and guardrail implementation
• Experience supporting technical direction for other engineers through architecture reviews, implementation guidance, and technical mentoring
• Ability to communicate complex technical decisions clearly to both engineers and non-technical stakeholders
• Experience operating in a build-own-operate product environment with strong expectations around reliability, supportability, and continuous improvement

Experience Level Senior Level