Job Title:  Manager | Engineering Foundry & Managed Services | Bengaluru | Engineering as a Service/ Operate

Lead AI Engineer/Architect 

As a Lead AI Engineer, you will architect and deliver enterprise-grade AI solutions, with a strong emphasis on GenAI, agent-based systems, and LLM orchestration. You will own the technical roadmap, guide engineering best practices, and serve as a thought leader in implementing scalable, secure, and efficient AI workflows. You will drive innovation, elevate the engineering bar, and play a pivotal role in shaping Ecolab’s applied AI capabilities. 

Core Responsibilities 

• Own end-to-end technical design and delivery of GenAI/agentic systems for internal or external applications 
• Architect multi-agent workflows using tools like LangChain, A2A protocols, and custom orchestration frameworks 
• Guide the design and tuning of prompt architectures, context strategies (e.g., with MCP), and hybrid RAG pipelines 
• Integrate AI services into enterprise platforms such as Azure Foundry, Databricks, and core business systems 
• Lead engineering pods, mentor engineers across levels, and drive technical alignment across product and platform teams 
• Push the boundaries of performance, latency, and accuracy through research-backed optimization 
• Define reusable templates, shared components, and internal GenAI SDKs 
• Enforce standards around ethical AI use, context control, prompt security, and hallucination mitigation 

Required Skills 

• 6+ years of experience in AI/ML/GenAI solutioning, with 3+ years in technical leadership 
• Deep proficiency in Python 3 with strong command over openai, pydantic, transformers, faiss, and langchain 
• Demonstrated experience in deploying scalable GenAI solutions with cloud-native design 
• Strong working knowledge of Azure cloud services, GitHub workflows, and CI/CD best practices 
• Experience in vector store optimization, token-level control, and prompt performance management 

Additional Software Engineering Skills: 

• Strong foundation in software engineering principles: data structures, algorithms, and design patterns 
• Experience architecting distributed systems and microservices for AI workloads 
• Hands-on expertise with CI/CD pipelines, automated testing frameworks, and GitOps practices 
• Proficiency in containerization and orchestration (Docker, Kubernetes) for production AI deployments 
• Familiarity with observability and monitoring tools (Prometheus, Grafana, ELK stack) for AI services 
• Experience with performance benchmarking, scalability testing, and optimization of AI systems 

Nice-to-have skills 

• Hands-on leadership in projects involving MCP, A2A orchestration, or custom agentic services 
• Contributor to open-source GenAI tooling or frameworks 
• Familiarity with prompt observability and compliance tooling 
• Experience in conducting code reviews, architecture walkthroughs, and internal capability building 
• Thought leadership via internal brown-bags, hackathons, or community talks