AI and Machine Learning Manager

Ecolab Food Safety & Hygiene Solutions Private Limited; Bengaluru; Presencial; INR 3.500.000 - 6.500.000; Jornada completa

Descripción de la vacante

Ecolab Food Safety & Hygiene Solutions Private Limited is seeking a Lead AI Engineer to own GenAI system architecture and delivery, integrating LLMs, multi-agent workflows, and embedding-based retrieval. You will lead cross‑functional pods, set engineering standards, and drive scalable, production-grade AI applications while mentoring teams and guiding strategic technical decisions.

This role also requires ensuring governance, security, and reliability across AI services, with collaboration

Formación

• Must have 6–8+ years in software/AI/ML engineering delivering GenAI applications.
• Proven experience leading the technical delivery of LLM-powered products or agent-based solutions.
• Strong Python and modern backend engineering experience.
• Hands-on with Azure OpenAI, LangChain, and orchestration frameworks.
• Experience with RAG, vector databases (e.g., Pinecone/FAISS) and cloud-native AI services.

Responsabilidades

• Architect GenAI systems using modular LLM APIs and embedding pipelines.
• Design autonomous agent workflows with context management and coordination.
• Optimize latency, performance, and accuracy via prompt strategies and caching.
• Lead reviews, enforce prompt safety and governance, align with security protocols.
• Collaborate with platform/product/engineering to define reusable patterns and scalable AI capabilities.
• Mentor teams on GenAI design principles, system reliability, and prompt lifecycle management.
• Develop reusability assets (SDKs, templates) to accelerate delivery velocity.

Conocimientos

Leadership

Cross-functional collaboration

Architectural design

Mentoring

Communication

Herramientas

Python

Azure OpenAI

LangChain

Semantic Kernel

Azure Functions

Docker

FastAPI

GitHub

Azure DevOps

Descripción del empleo

Lead AI Engineer

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