Engineering Manager, Applied AI & Machine Learning Engineering

• At least three years of experience in software engineering, AI/ML, or a related field
• At least three years of experience in people management, engineering management, or technical program leadership
• Working knowledge of AI/ML modeling, systems and the development lifecycle
• Experience managing multiple technical projects with shared resources and cross-team dependencies
• Strong skills in capacity planning, resource allocation, project execution, risk management, and stakeholder communication
• Experience supporting the performance and career development of technical employees
• Ability to understand technical discussions and work effectively with MLE and MLOps teams
• Experience working with distributed or geographically dispersed teams
• English - Upper Intermediate or higher; Ukrainian - fluent

• Experience managing or supporting MLE and MLOps teams
• Experience working in a matrix organization with separate people, project, product, and technical leadership roles
• Experience with planning or allocating engineers across multiple projects
• Familiarity with production ML, generative AI, semantic search, summarization, prediction, or similar capabilities
• Familiarity with technologies such as Kubernetes, Docker, Kafka, Airflow, Snowflake, and cloud platforms
• Experience conducting performance reviews and building employee development plans
• Experience working with staffing partners, contractors, or blended internal and external teams
• Experience in fintech, SaaS, market data, or another data-intensive industry

Join the team of experts who create custom, cutting-edge tech solutions for world-renowned businesses, fueling clients growth. Unleash your potential, tackle new challenges, and be part of a team that values your skills and contributions. Focus on long-term impact and building tailored, long-lasting partnerships with our clients.

Experience an agile and flexible working environment

Work fully remote around the globe, or in a hybrid work model from our office hubs which are equipped with everything needed for comfortable work. Enjoy a flexible working schedule and create work and rest balance for yourself. Empower yourself with a stable workload and a stable income, focusing on lasting cooperation and high-performance culture.

Embrace the opportunity for personal and professional growth

Take advantage of performance review and career path. Elevate your skills with mentorship program, personal development plan, and individual learnings through the corporate library, public speaking support, and more.

Feel cared about

Be among like-minded people and feel supported with collaboration, open communication and mutual respect. Join company-wide tech and cultural events, and contribute to meaningful CSR initiatives that resonate with your values.

And now we are looking for a Engineering Manager, Applied AI & Machine Learning Engineering to join us as part of our team.

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.

As an Engineering Manager, AI/ML, you will support a cross-functional team of MLE and MLOps engineers working across several AI/ML initiatives.

Your primary focus will be resource planning, execution, and people management. You will work closely with project leads, technical leads, functional managers, and product partners to ensure projects are appropriately staffed, plans are realistic, risks are visible, and teams can deliver effectively.

You will manage capacity and resource allocation across multiple projects, help coordinate dependencies and priorities, and provide stakeholders with a clear view of delivery progress. Project and technical leads will continue to own product direction and technical decisions.

You will also support the career growth and performance of team members. This includes gathering feedback, coaching employees, creating development plans, and contributing to performance, promotion, and compensation reviews.

You should have enough AI/ML experience to understand the work, communicate effectively with engineers, and identify technical delivery risks. However, this is not expected to be a hands-on engineering or architecture role.

• Manage resource allocation and capacity planning across multiple AI/ML projects and operational commitments
• Partner with project and technical leads to create realistic delivery plans, milestones, and staffing models
• Maintain a clear view of project progress, dependencies, risks, and resource constraints
• Help resolve delivery blockers, priority conflicts, and cross-team dependencies
• Facilitate quarterly and ongoing planning across MLEs and MLOps teams
• Provide clear delivery and staffing updates to functional managers and other stakeholders
• Manage and coach engineers through regular one-to-one meetings and career discussions
• Gather and synthesize feedback from project leads, technical leads, and cross-functional partners
• Create development plans and support performance, promotion, and compensation review processes
• Identify team capability gaps and contribute to hiring, onboarding, retention, and workforce planning
• Foster an inclusive, collaborative, and high-performing team environment
• Apply Agile, Lean, and Fast-Flow practices to improve team effectiveness and delivery predictability

Flexible schedule with participation in team ceremonies and cross-functional collaboration. Time zone: Europe/Kyiv.

• Generative AI & LLMs: Develop and maintain LLM-based solutions for generative features.
• Classic ML Models: Develop and maintain predictive and classification models for financial data
• Vector Databases: Implementing efficient storage and retrieval systems for high-dimensional data embeddings.
• Embeddings: Developing hybrid models to extract insights from diverse data sources.
• High-Volume Data Management: Engineering systems capable of handling massive, real-time data streams.