Manager AI-ML

ecolab; India; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

ecolab is seeking a Manager - AI Engineering in India to lead a multidisciplinary team delivering GenAI-powered solutions. You will guide LLM-powered systems, agent orchestration, and MLOps while ensuring scalable, secure, and production-ready AI capabilities.

You will work closely with Product Managers, Solution Architects, and Platform teams to translate business goals into durable engineering outcomes and maintain high delivery quality and technical growth within the team.

Qualifications

• 10+ years in software or AI engineering with production systems.
• 4–5+ years delivering GenAI/LLM solutions in enterprise settings.
• Proven leadership of multidisciplinary teams with ownership.
• Strong Python and backend engineering foundations.
• Experience with ML model delivery, evaluation, and observability.

Responsibilities

• Lead and mentor AI Engineers and Data Scientists across experience levels.
• Own end-to-end delivery of GenAI and ML-enabled solutions.
• Guide decisions on LLM systems, RAG, and ML pipelines.
• Oversee model development from discovery to production.
• Ensure SDLC, testing, release readiness, and production support.
• Collaborate with product managers and platform teams to align roadmaps.

Skills

Team leadership

Python

ML engineering

Cloud platforms

Tools

API-first architecture

MLOps

LLM tooling

Job description

ROLE SUMMARY

As Manager - AI Engineering, you will lead a multidisciplinary team of AI Engineers and Data Scientists delivering GenAI-powered solutions, agentic AI systems, LLM-powered applications, machine learning models, and retrieval-enabled intelligent products. This role combines technical leadership, people management, delivery accountability, and product ownership. You will be responsible for ensuring that the team delivers scalable, secure, supportable, and business-relevant AI and ML solutions from discovery through production operation and continuous improvement.

This is not a lightweight line-management role. The successful candidate must be technically strong enough to guide senior engineers, data scientists, and technical leads, make sound architectural and implementation trade-offs, and raise the engineering bar of the team. You are expected to provide hands‑on technical direction across LLM-powered systems, agent orchestration, RAG, ML model development, MLOps, cloud‑native application delivery, integrations, observability, and SDLC discipline, while also building a culture of accountability, ownership, experimentation rigor, and engineering excellence.

You will work closely with Product Managers, Solution Architects, Lead AI Engineers, Lead Data Scientists, Platform teams, DevOps, Security, and business stakeholders to translate business goals into implementable engineering work and durable product outcomes.

KEY RESPONSIBILITIES

• Lead and mentor a team of AI Engineers and Data Scientists across varying experience levels, including senior engineers and technical leads, ensuring strong delivery quality, modeling rigor, engineering discipline, and technical growth
• Own end-to-end delivery of GenAI, agentic AI, and ML-enabled solutions - from discovery, design, and backlog shaping through development, testing, deployment, monitoring, and continuous improvement
• Guide architectural and design decisions across LLM-powered systems, embeddings pipelines, retrieval-augmented generation (RAG), agent orchestration, tool-enabled workflows, ML model pipelines, APIs, and cloud-native application services
• Oversee the design, development, evaluation, and operationalization of machine learning models for predictive, classification, recommendation, anomaly detection, forecasting, optimization, or other business use cases where applicable
• Ensure strong practices across feature engineering, experimentation, validation, model performance assessment, explainability, drift awareness, and MLOps readiness
• Translate business and product priorities into executable engineering and data science plans, technical workstreams, model delivery milestones, and sustainable release outcomes
• Review and challenge implementation choices to ensure systems and models are scalable, secure, observable, cost-aware, and maintainable
• Partner with Product Managers and engineering leadership to prioritize work, manage technical dependencies, and align delivery with roadmap goals
• Establish strong SDLC and build-own-operate practices within the team, including design reviews, code reviews, model reviews, automated testing, release readiness, production support, reliability improvement, and technical debt management
• Drive reuse and productivity by scaling frameworks, shared components, prompt templates, orchestration patterns, feature templates, modeling utilities, evaluation frameworks, and internal accelerators across the team
• Promote a culture of responsible AI and operational excellence, emphasizing security, token and cost governance, model safety, quality, observability, reproducibility, and supportability
• Coordinate with cloud platform, DevOps, Security, Integration, Architecture, and data teams to ensure enterprise readiness of all deployments
• Own hiring, onboarding, coaching, performance development, and growth plans for both the AI Engineering and Data Science team members
• Track and communicate KPIs related to delivery, GenAI adoption, model quality, latency, business impact, reliability, experimentation outcomes, and production performance
• Act as the primary technical and delivery escalation point for the team, helping remove blockers and resolve design, modeling, execution, and operational issues

Required Qualifications

• 10+ years of experience in software engineering, AI / ML engineering, data science, solution engineering, or technology delivery, including strong experience building and operating production-grade intelligent systems
• 4 to 5 + years of experience delivering or leading AI / ML / GenAI / LLM-powered solutions in enterprise or product environments
• Proven experience leading multidisciplinary teams or technical pods delivering LLM-powered products, agentic AI workflows, machine learning models, or AI-enabled application capabilities, with accountability for both technical quality and delivery outcomes
• Strong technical depth in Python, modern backend engineering, machine learning solution delivery, API-first architectur