Lead Machine Learning Engineer

Key Responsibilities

• Design, build, and launch GenAI-powered applications including AI assistants, copilots, document intelligence, workflow automation, and decision-support solutions.
• Identify high-impact opportunities where AI can improve productivity, operational efficiency, service quality, customer experience, and business outcomes.
• Take AI applications from concept through production, collaborating with Product, Engineering, Design, Security, and business teams.
• Lead hands-on technical execution across application architecture, model selection, prompt engineering, retrieval, orchestration, APIs, data pipelines, and user-facing experiences.
• Translate business requirements into scalable and measurable machine learning and AI solutions.
• Establish success metrics and continuously optimize solutions based on real-world user feedback and business impact.

• Architect reliable GenAI applications using modern approaches such as RAG, agentic workflows, tool use, structured outputs, retrieval, grounding, and fine-tuning where appropriate.
• Design systems that effectively combine frontier models, open-source models, smaller task-specific models, and deterministic components based on the specific use case.
• Develop strong grounding mechanisms using enterprise knowledge and relevant business data.
• Build production systems with appropriate observability, monitoring, versioning, fallback mechanisms, security, privacy, and operational ownership.
• Design for reliability, scalability, latency, cost efficiency, and maintainability.
• Stay current with advances in AI/ML and apply emerging techniques pragmatically where they deliver meaningful improvements.

• Define practical evaluation frameworks for GenAI applications covering accuracy, relevance, groundedness, safety, latency, cost, user trust, adoption, and business impact.
• Establish automated and human-in-the-loop evaluation processes for AI applications.
• Use LLM evaluation and observability platforms such as LangFuse, Arize, or similar tools.
• Monitor production performance and identify opportunities to improve model quality, reliability, and efficiency.
• Establish appropriate safeguards, fallback paths, and quality controls for production AI systems.

• Provide technical leadership across the AI/ML application development lifecycle.
• Make pragmatic architecture and technology decisions while balancing quality, speed, security, and cost.
• Mentor engineers and contribute to engineering standards, best practices, and technical direction.
• Partner with cross-functional teams to ensure AI solutions are usable, secure, reliable, and aligned with business objectives.
• Take ownership of production outcomes, including launch quality, reliability, user feedback, adoption, and measurable impact.

Required Experience & Qualifications

• 8+ years of experience building applied AI/ML-based intelligent software systems.
• 2+ years of practical Generative AI application experience.
• At least one production GenAI application that has been deployed to real users at meaningful scale.
• Proven experience taking GenAI solutions beyond PoC/prototype into production.
• Strong ownership of production quality, reliability, cost optimization, user feedback, adoption, and measurable business impact.
• Strong understanding of designing LLM applications using an appropriate combination of:
• RAG
• Agentic workflows
• Tool use
• Structured outputs
• Retrieval and grounding
• LLM orchestration
• Frontier and open-source models
• Fine-tuning
• Task-specific models
• Deterministic systems
• Experience with modern AI application frameworks and LLMOps tools such as LangGraph, LangChain, LlamaIndex, and leading LLM APIs.
• Strong programming and software engineering capabilities with the ability to build and deploy production-quality AI applications.
• Experience using AI-native development tools such as Cursor, Claude Code, or similar tools is preferred, with strong judgment around code quality, security, and production reliability.

Good-to-Have Experience

• GraphRAG
• Long-context architectures
• Model routing
• Semantic and intelligent caching
• Model cascades
• PEFT / LoRA / QLoRA
• Knowledge retrieval and grounding
• Model distillation
• Open-source model deployment
• Advanced LLM evaluation and observability
• Enterprise AI security and governance

Must-Have Skills

• Machine Learning
• Generative AI (GenAI)
• Production AI/ML Systems
• LLM Applications
• Python / Software Engineering
• AI Application Architecture

Good-to-Have Skills

• End-to-End Production AI
• Fine-Tuning
• RAG
• Agentic AI
• LLMOps
• LangGraph / LangChain / LlamaIndex
• Model Evaluation & Observability
• GraphRAG
• PEFT / LoRA / QLoRA