Principal, Lead Engineer AI

Principal, AI/ML Engineer

The Principal, AI/ML Engineer leads the design, delivery and continuous improvement of AI and ML solutions within their area of responsibility, aligning engineering activities to the Bank’s broader AI strategy and long‑term objectives. The role combines strong technical leadership with a clear focus on delivery outcomes, ensuring solutions are scalable, reliable and aligned to business value.

The Principal operates with a clear understanding of the wider technology landscape, contributing to strategic direction while taking accountability for translating this into practical, high‑quality engineering outcomes across squads.

AI/ML Engineering Direction

Leads the definition and execution of AI/ML engineering direction across one or more squads, ensuring alignment with the Bank’s AI strategic vision, as set out by the AI Capability Lead, and architectural principles.

Accountable for shaping and evolving technical approaches for AI/ML systems within their area, ensuring solutions are consistent with agreed patterns, standards and platforms.

Contributes to the development and adoption of reference architectures for key AI components, including LLMs, vector search and inference services, ensuring solutions are scalable and maintainable.

Technical Discovery and Risk Management

Leads technical discovery activities such as proofs of concept, feasibility assessments and vendor evaluations, translating findings into clear recommendations and delivery plans.

Supports design reviews and technical checkpoints for AI initiatives, ensuring risks are identified early and responsible AI considerations such as fairness, privacy and safety are incorporated into delivery.

Guides squads in adopting best practices in observability, incident management and service‑level design to ensure reliable and resilient AI/ML services.

Back‑log Management and Mentoring

Works closely with Product Owners and Platform teams to prioritise and sequence backlogs, balancing delivery of business value with ongoing improvement and technical sustainability.

Mentors engineers within and across squads, contributing to the development of capability in data and ML engineering and fostering knowledge sharing.

Stakeholder Communication

Engages with senior stakeholders to communicate technical direction, delivery progress and key risks in a clear and structured manner.

Maintains an understanding of commodity AI solutions and ensures appropriate security and governance guardrails are applied when introducing new capabilities.

Software Design and Development

Leads the design and delivery of AI/ML solutions within their scope, ensuring high standards of performance, scalability and maintainability.

Designs and oversees implementation of end‑to‑end ML workflows, including data pipelines, feature engineering, model development and deployment, working closely with AI architecture roles to ensure alignment with enterprise patterns and standards.

Oversees the end‑to‑end AI/ML lifecycle within their scope, from prototyping through to deployment and optimisation.

Ensures that solutions consider performance, interpretability and appropriate use of AI, in line with organisational standards and expectations.

Promotes the adoption of modern AI techniques, including machine learning, natural language processing and advanced analytics, where appropriate to business needs.

Champions effective software engineering practices, including modular design, reuse of components and adherence to coding standards.

Identifies and drives opportunities to enhance existing solutions or introduce innovation through the application of emerging technologies.

Quality Assurance

Defines and embeds appropriate testing and validation approaches for AI/ML solutions, including model evaluation and performance testing.

Ensures that monitoring and benchmarking practices are in place so that models perform reliably and consistently in production.

Supports the implementation of processes to identify and address model drift, data issues and potential bias.

Promotes a culture of quality within squads, ensuring solutions meet business requirements and relevant regulatory standards.

Represents the team in internal and selected external technical forums where appropriate, contributing to knowledge sharing and continuous improvement.

Operations, Maintenance, Support and Documentation

Leads the implementation of MLOps practices within their area, supporting efficient model deployment, monitoring and lifecycle management.

Ensures that CI/CD practices are applied to AI/ML workflows to improve delivery speed and reliability.

Oversees the ongoing performance of production AI systems within their scope, addressing issues related to scalability and stability.

Ensures that appropriate documentation is in place for models, datasets and key technical decisions, supporting maintainability and transparency.

Data and Architecture

Contributes to the evolution of AI/ML architecture within their area, ensuring solutions are scalable, efficient and aligned with enterprise direction.

Guides the optimisation of data pipelines, feature stores and model serving approaches to support effective AI delivery.

Supports the evaluation and adoption of cloud‑based AI/ML services, ensuring choices are aligned with technical and business requirements.

Ensures that ethical AI principles and security considerations are embedded in solution design and implementation.

Knowledge, Skills, Experience & Qualifications

Holds a degree in Computer Science, Machine Learning, or a related technical field, or equivalent industry experience, with a strong focus on AI and ML systems.

Brings significant hands‑on experience developing and delivering production‑grade AI/ML solutions within cloud environments, ideally Azure.

Proven ability to design, implement and support resilient AI/ML solutions in production environments, ensuring reliability and scalability.

Demonstrates strong practical experience with Azure‑based AI services, including Azure OpenAI, AI Search and AI Studio.

Well‑versed in modern machine learning approaches, MLOps practices and cloud‑based AI architectures.

Proficient in Python, common machine learning frameworks, distributed processing concepts and core MLOps practices.

Experience designing APIs, building microservices and implementing end‑to‑end ML pipelines on cloud platforms.

Demonstrates experience of cloud platforms such as Azure and AWS, with experience supporting and maintaining cloud‑based infrastructure.

Hands‑on experience working with LLM‑based solutions, including Retrieval‑Augmented Generation techniques and prompt engineering approaches.

Hands‑on experience working with data processing frameworks and platforms, including batch and streaming pipelines.

Practical understanding of MLOps processes, including model lifecycle management, deployment approaches, monitoring and performance optimisation.

Experience supporting model serving, feature engineering and solution optimisation to meet performance and accuracy requirements.

Clear understanding of AI ethics, model governance and explainability principles, and their application in delivery.

Ability to contribute to technical direction and support alignment across teams, working effectively with Product Owners, architects and engineering leads.

Strong understanding of cloud security fundamentals, compliance considerations and cost awareness when delivering AI/ML solutions.

Fluent in spoken and written English, with an ability to work effectively across diverse and multicultural teams.

Able to communicate clearly with both technical and non‑technical stakeholders, tailoring messages to the audience.

Confident in making informed technical decisions within their scope, considering delivery constraints, risks and longer‑term implications.

Experience contributing to the adoption of coding standards, CI/CD practices and quality approaches within teams.

Experience contributing to technical documentation, knowledge sharing and internal communities of practice.

Demonstrates strong team leadership behaviours, supporting and mentoring engineers and contributing to a positive and collaborative engineering culture.

Experience working with data engineering processes, model training workflows and real‑time or near real‑time AI solutions.

Understanding of data governance practices and regulatory considerations relevant to AI/ML delivery.

Job Segment

Testing, Computer Science, Quality Assurance, Embedded, Cloud, Technology

Equal Employment Opportunity

Diversity is one of the Bank’s core values which are at the heart of everything it does. As such, the EBRD seeks to ensure that everyone is treated with respect and given equal opportunities and works in an inclusive environment. The EBRD encourages all qualified candidates who are nationals of the EBRD member countries to apply regardless of their racial, ethnic, religious and cultural background, gender, gender identity, sexual orientation, age, socio‑economic background or disability.

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