About Us
At SPD Technology, we bring together a team of like-minded people who are driven by the desire to bring value through their work, united in their commitment to high performance and delivering custom, cutting-edge tech solutions that drive clients’ growth. We empower our people with a culture of excellence and enable them with the opportunity to uphold their accountability to contribute on each level. We value humanity and collaboration, encourage professional and personal growth, and foster a supportive and flexible work environment where everyone’s contribution is welcomed.
And now we are looking for a Machine Learning Engineering Lead to join us as part of our team.
About the role
PitchBook — a platform for investment professionals. Our software provides access to data and the analytical tools to get answers fast and discover promising opportunities. Uncovers actionable insights and trends hidden within the financial data of more than three million companies. Users all over the world include large corporations, start-ups, venture capital and private equity firms, investment banks, and many others.
We’re looking for a highly hands-on Machine Learning Engineering Lead to drive the delivery of production-grade ML services. Your mission is to act as the primary technical driver for a team that bridges the gap between data science and enterprise software engineering, focusing on Machine Learning as a Service (MLaaS). As a coding leader, you will work with your team to deliver:
Model as a Service: Design, code, and productionalize models into robust, model-driven services for use in product features.
Service Enhancement: Directly implement advanced ML integrations to improve existing products.
End-to-End MLDLC: Actively build and manage the pipelines that take models from experimentation to full production-grade deployment.
The ideal lead is an experienced software engineer at their core, balancing high-level system design with specialized ML awareness. You are expected to be a hands-on contributor while leading the technical standard for the team.
Hands-on Engineering Excellence: Lead the design, breakdown, and implementation of scalable, secure, and maintainable software solutions. Write clean, efficient, and well-documented code.
Full Cycle Delivery: Take ownership of the transition from data science experimentation to productionized services, coding the integration layers, communication interfaces, and cloud deployment..
Best Practices via Leading by Example: Lead the engineering SDLC practices through rigorous code reviews, pair programming, and automated testing.
Infrastructure Mastery: Utilize, build, and adjust MLOps infrastructure (CI/CD, Kubernetes, Docker, clouds) to optimize the team’s delivery pace.
Team setup: 1 Engineering Manager, 6 ML & AI Engineers.
Technical Stack: Python, Classic ML, LLM toolkits, Langchain, Langsmith, AWS, GCP, AWS Sagemaker, Kafka, Prometheus, Grafana, Fast API.
Work Environment: The role offers a flexible work schedule, allowing you to adapt your working hours with the requirement to attend all team meetings. The team follows a Scrum-based Agile methodology.
As a qualified expert You will
Technical Execution & Implementation (Primary Focus)
Scalable Systems: Lead design and build high-performance, production-grade systems that handle real-time, high-volume data processing.
Integration Ownership: Serve as the lead developer collaborating with cross-functional product delivery teams to seamlessly integrate ML models into the broader enterprise infrastructure.
Complex Troubleshooting: Dive deep into the codebase to foresee or troubleshoot and resolve the most complex technical, architectural, and deployment issues.
Innovation: Experiment with and implement emerging trends in ML, Agentic AI, LLMs, and GenAI to identify practical opportunities for product improvement.
Technical Leadership & Enablement
Technical Mentorship: Act as the primary technica
