Lead AI Engineer

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 Ecolabs 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