Original listing text, shown exactly as published by the company.
Your Mission
As a Lead Machine Learning Engineer, you will be the technical authority for our most ambitious client projects. You will set the technical vision, guide teams of talented engineers, and translate complex business challenges into cutting-edge AI solutions on Google Cloud. You'll be an advocate of high-quality engineering and best-practice in production software as well as rapid prototypes.
This is a hands-on leadership role where you will not only architect solutions but also actively lead client discussions and oversee project delivery from start to finish. Your responsibilities will involve building trusted relationships with prospects, finding creative ways to use machine learning to solve problems, scoping projects, and overseeing the delivery of these engagements.
To be successful, you will need strong ML & Data Science fundamentals and will know the right tools and approach for each ML use case. You'll be comfortable with model optimization and deployment tools and practices. Furthermore, you'll also need excellent communication and consulting skills, with the desire to meet real business needs and deliver innovative solutions using AI & Cloud.
What You’ll Do
- Drive Technical Strategy from Pre-Sales to Delivery: Act as the lead technical authority in high-stakes engagements. You will partner with our commercial team to architect winning solutions, and then lead the delivery of enterprise-grade systems—such as GenAI agents for financial institutions, real-time recommendation engines for global retailers, or predictive maintenance models for the manufacturing sector.
- Architect & Implement Production-Grade Solutions: Own the complete technical lifecycle for your projects. You will design end-to-end ML architectures on GCP, implement robust MLOps pipelines using Infrastructure-as-Code (Terraform), and ensure all solutions—from sophisticated multi-agent systems to classical models—are optimized for performance, scalability, and security.
- Shape Our Technical Standards: Collaborate directly with the Head of Delivery to define the technical DNA of our ML practice, evolving the best practices, architectural patterns, and standards that ensure excellence across the company.
- Lead Strategic Internal Initiatives: Spearhead the development of internal accelerators and reusable frameworks that enhance our delivery capabilities and solidify our position as industry leaders.
- Cultivate Engineering Talent: Formally mentor and coach our junior and mid-level engineers, elevating the team's collective skill set through rigorous code reviews, technical guidance, and career development.
What You’ll Bring
- Experience: You have 7+ years of professional experience in machine learning and software engineering, with at least 2 years in a formal or informal leadership capacity (e.g., tech lead, project lead, or senior mentor).
- Cloud & Systems Architecture Expertise: You possess a proven ability to architect and deploy scalable, production-grade ML solutions on a major cloud platform (GCP is a significant asset). This includes hands-on experience with Infrastructure-as-Code tools (e.g., Terraform) and designing for distributed computing.
- Expert-Level Engineering Craftsmanship: You have deep, hands-on expertise in Python for backend ML systems and a mastery of software engineering best practices (e.g., clean architecture, robust testing, CI/CD). You can fluently design and build REST APIs (e.g., using Flask/FastAPI) and are proficient in SQL for complex data manipulation.
- Consultative Communication & Mentorship: You have an exceptional ability to communicate complex technical concepts to diverse audiences, from C-level stakeholders to junior engineers. You excel at leading technical discussions, presenting solutions, and mentoring teammates to elevate their skills.
Bonus Points If You Have
- Prior experience in a client-facing or consulting role.
- Professional Google Cloud certifications (e.g., Professional Machine Learning Engineer).
- Deep experience with the broader MLOps ecosystem (e.g., Kubeflow, Vertex AI Pipelines, MLflow).
- Experience building interactive demos for ML models (e.g., using Streamlit, Gradio).
