ML Lead Engineer 2

Responsibilities: Design and build production-grade LLM and agentic AI systems.Extensive experience in context engineering and harness engineering.Design agent workflows involving tools, APIs, retrieval, memory, policies, and human-in-the-loop escalation.Build context engineering layers that retrieve, rank, structure, and manage business context for LLM applications.Good to have exposure to the Rust programming language.Develop prompt, tool, and workflow orchestration patterns for reliable AI behaviour.Create evaluation harnesses for LLM applications, including regression tests, golden datasets, automated scoring, trace replay, and human review workflows.Define and track metrics for task success, accuracy, cost, latency, safety, and reliability.Integrate LLM systems with enterprise data sources, APIs, vector databases, search systems, and operational platforms.Implement observability for AI systems, including tracing, prompt/version tracking, tool-call monitoring, and failure analysis.Partner with product, data science, engineering, analytics, security, and business teams to convert ambiguous problems into scalable AI solutions.Lead architecture reviews, mentor engineers, and establish best practices for LLM application development.Evaluate models, frameworks, vendors, and infrastructure options based on quality, latency, cost, privacy, and maintainability. Requirements: 7 - 9 years of experience in Machine learning and data modelling.Strong experience in machine learning engineering, software engineering, data science engineering, or AI platform development.Hands-on experience building production ML, LLM, NLP, search, recommendation, or decision-support systems.Strong Python programming skills and experience building production services or APIs.Experience with LLM application patterns such as RAG, tool calling, structured outputs, workflow orchestration, or AI agents.Experience designing evaluation frameworks for ML or LLM systems.Understanding of prompt engineering, context design, retrieval quality, and model behaviour testing.Experience with cloud infrastructure, CI/CD, monitoring, logging, and deployment practices.Ability to reason about trade-offs across accuracy, latency, cost, reliability, privacy, and user experience.Strong communication skills and ability to lead technical work across teams. Responsibilities: Design and build production-grade LLM and agentic AI systems.Extensive experience in context engineering and harness engineering.Design agent workflows involving tools, APIs, retrieval, memory, policies, and human-in-the-loop escalation.Build context engineering layers that retrieve, rank, structure, and manage business context for LLM applications.Good to have exposure to the Rust programming language.Develop prompt, tool, and workflow orchestration patterns for reliable AI behaviour.Create evaluation harnesses for LLM applications, including regression tests, golden datasets, automated scoring, trace replay, and human review workflows.Define and track metrics for task success, accuracy, cost, latency, safety, and reliability.Integrate LLM systems with enterprise data sources, APIs, vector databases, search systems, and operational platforms.Implement observability for AI systems, including tracing, prompt/version tracking, tool-call monitoring, and failure analysis.Partner with product, data science, engineering, analytics, security, and business teams to convert ambiguous problems into scalable AI solutions.Lead architecture reviews, mentor engineers, and establish best practices for LLM application development.Evaluate models, frameworks, vendors, and infrastructure options based on quality, latency, cost, privacy, and maintainability. Requirements: 7 - 9 years of experience in Machine learning and data modelling.Strong experience in machine learning engineering, software engineering, data science engineering, or AI platform development.Hands-on experience building production ML, LLM, NLP, search, recommendation, or decision-support systems.Strong Python programming skills and experience building production services or APIs.Experience with LLM application patterns such as RAG, tool calling, structured outputs, workflow orchestration, or AI agents.Experience designing evaluation frameworks for ML or LLM systems.Understanding of prompt engineering, context design, retrieval quality, and model behaviour testing.Experience with cloud infrastructure, CI/CD, monitoring, logging, and deployment practices.Ability to reason about trade-offs across accuracy, latency, cost, reliability, privacy, and user experience.Strong communication skills and ability to lead technical work across teams.