Lead Machine Learning Engineer

Role Overview

• Develop sophisticated, production-scale AI systems, including multi-step agentic workflows and multi-agent orchestration platforms.
• Build tools & agents with advanced capabilities in reasoning, planning, and adaptive tool utilization to address complex business challenges.
• Drive complete ownership of the AI/ML lifecycle – encompassing implementation, comprehensive testing, deployment, and continuous operational monitoring – delivering projects on schedule and to specification.
• Produce high-quality, maintainable code for model training pipelines, evaluation frameworks, and inference services that meet production standards.
• Partner strategically with cross-functional stakeholders including product leaders, data scientists, application teams, vendors, and partners to align on requirements, iterate on solutions, and deliver successful outcomes.
• Provide hands-on technical leadership, driving architectural decisions and championing best practices across AI development, LLMOps, quality assurance, and production deployment.
• Design and implement responsible AI frameworks including hallucination detection, safety guardrails, comprehensive evaluation systems, and observability infrastructure to ensure model reliability, accuracy, and ethical deployment.
• Establish comprehensive evaluation frameworks for Large Language Models and agent-based systems, measuring model quality, task success rates, safety compliance, and operational effectiveness.
• Proactively identify and resolve technical blockers that could impact project timelines or deliverables.
• Communicate technical strategy and progress to executive leadership and key stakeholders with clarity and confidence.
• Engage directly in development and problem-solving, particularly on high-complexity technical challenges, to maintain project velocity and quality.
• Drive innovation through research and experimentation with emerging AI technologies and frameworks, evaluating and integrating new capabilities that advance our platform.

Requirements

• 7+ years of proven expertise in designing, building, and deploying AI/ML solutions at scale, with 1-2 years of production experience in Generative AI technologies.
• Strong foundation in machine learning including statistical modeling, supervised and unsupervised learning algorithms.
• Advanced skills in prompt engineering with deep understanding of optimization techniques and best practices for LLM interactions.
• Expert-level programming proficiency in Python and AI/ML development ecosystems.
• Deep expertise in modern AI frameworks including LLM application development and agentic systems (LangChain, CrewAI, or similar).
• Comprehensive MLOps experience with hands-on implementation of CI/CD pipelines, model monitoring, versioning, and lifecycle management for both models and agent-based systems.
• Production deployment experience on major cloud platforms (AWS, Azure, or GCP) with demonstrated ability to architect and scale cloud-native ML solutions.
• Versatile ML skillset spanning traditional techniques (classification, regression, clustering) and cutting-edge deep learning approaches.
• Production-grade generative AI experience deploying and maintaining LLMs and multi-modal models in live environments.
• Exceptional analytical capabilities with a track record of solving complex technical problems and thriving in ambiguous, rapidly-evolving situations.
• Proficiency with industry-standard ML libraries including PyTorch, TensorFlow, Scikit-learn, NumPy, and Pandas.
• Outstanding communication and collaboration skills with ability to translate complex technical concepts for diverse audiences and drive cross-functional alignment.
• Success partnering across organizational levels from individual contributors to senior leadership, building trust and delivering results.
• Proven ability to influence and lead in matrix organizations where collaboration and relationship-building are essential to achieving outcomes.

Tech Stack

• AWS
• Azure
• Cloud
• Google Cloud Platform
• Numpy
• Pandas
• Python
• PyTorch
• Scikit-Learn
• Tensorflow

Benefits

• A bonus and/or long-term incentive units may be provided as part of the compensation package, in addition to the full range of medical, financial, and/or other benefits