Director- Machine Learning Engineering

Job Description

Director of Software Development to lead the machine learning engineering team responsible for building our next-generation AI applications on the cloud. In this role, you will collaborate closely with product teams, business teams, applied science team, customer success teams, and other stakeholders to ensure alignment on priorities, timelines, and deliverables. You will drive the development efforts and ensure that customer engagements are fully supported with high-quality, scalable solutions that deliver exceptional results. Key Responsibilities Leadership and Team Management: Lead, mentor, and manage machine learning engineering team comprising senior engineers and leads. Build and scale a team of 10-15 top-notch engineers, providing regular mentoring and creating clear career development paths. Serve as a role model and default mentor, setting best practices for development, optimizing code quality, and promoting technical excellence within the team. Customer Engagement & Deliverable Management: Design Collaborate closely with product and customer success teams to align on customer priorities and ensure that development timelines meet client expectations. Ensure that customer feedback is integrated into the development lifecycle, adjusting priorities as necessary to align with changing business needs and deliverable timelines. Act as the primary technical point of contact for customer engagements, helping to set clear expectations for deliverables, project status, and delivery timelines. Drive successful customer-facing product launches by ensuring that the engineering team delivers high-quality, on-time solutions that meet or exceed customer requirements. Product Development & Strategy: Oversee the development of AI application development, ensuring smooth integration of ML/ AI models into production environments, and guide the team in building feature pipelines for model serving. Drive product strategy in collaboration with product management, business development, and operations teams, aligning engineering efforts with business goals and customer priorities. Provide technical leadership on key projects, ensuring architectural robustness, scalability, and flexibility in designs. Scalability & Best Practices: Architect, design, and implement scalable solutions that align with long-term product vision, leveraging cloud-native technologies and microservices architectures. Oversee the management and optimization of end-to-end MLOps pipelines for data collection, model training, validation, and monitoring, while ensuring team collaboration and effective resource allocation. Drive the implementation of model compression, quantization, and distributed training techniques to enhance performance, encouraging innovative solutions from team members. Champion the adoption of industry best practices in development, ensuring code quality, scalability, and performance. Set up and drive best practices for machine learning engineering, ensuring the team adopts these standards consistently. Collaborate with product management, business, applied science, UX/UI teams, and business analysts to plan and estimate projects in an Agile environment. Stay current with advancements in ML technologies and lead ongoing enhancement initiatives within the team, encouraging team participation in the ML community Engage closely with customers to ensure timely alignment on product delivery and key milestones, proactively managing risks and ensuring transparency on timelines. Work in close partnership with designers, product managers, and other stakeholders to navigate uncertainties and deliver scalable, high-performance solutions in a rapidly evolving environment. Skills & Qualifications: Advanced Experience: 12-16 years of experience in software development, with deep expertise in machine learning frameworks and back-end (e.g., Java, Python) technologies. Leadership: Proven track record of leading engineering teams focused on customer facing product development, including scaling teams and managing senior engineers and managers. Customer-Centric Focus: Strong experience engaging directly with customers to align on priorities, manage timelines, and ensure that deliverables meet both customer and business objectives. Technical Expertise: 9+ years of experience in ML software engineering, with a strong portfolio of deployed ML models for various use cases, including batch, streaming, and real-time. Expertise in building and scaling microservices architectures, cloud-based environments (AWS, Azure, GCP), and modern full-stack frameworks. Knowledge of machine learning frameworks (e.g., TensorFlow, PyTorch) Strong understanding of data processing and ETL tools (e.g., Apache Spark, Kafka). Excellent knowledge of REST principles, database systems (RDBMS, NoSQL), and performance optimization techniques. Problem-Solving & Strategic Thinking: Strong problem-solving skills with the ability to diagnose technical challenges and devise scalable solutions. Strategic thinker who can manage business goals while fostering technical growth within the team. Collaboration & Communication: Strong interpersonal skills with the ability to work cross-functionally, collaborating with product managers, designers, and business development teams to deliver technology solutions. Preferred Qualification Experience managing large-scale SaaS platforms, production systems and distributed computing environments with a focus on scalability and performance. Hands-on experience with Agile methodologies and DevOps practices. An innovative mindset with a track record of developing solutions that yield significant business improvements or patents. Automotive industry experience is a plus but not required.