The posting
Engineering & Delivery Manager (Azure, Data & AI) – Job Description
Position Overview
We are hiring an Engineering & Delivery Manager to lead a team that builds and runs scalable backend services and React applications on Azure, together with the data and AI capabilities that power them. You will own delivery end to end: team leadership, planning, quality and stakeholder communication, across application engineering, data engineering and applied AI (ML and GenAI).
This is a hands-on leadership role. You will manage and grow the team, stay close enough to the architecture and codebase to guide technical decisions, and be accountable for shipping reliable, secure, production-grade solutions on time and within scope.
Key Responsibilities
- Team leadership: Lead, mentor and grow a team of software, data and AI engineers. Set clear expectations, give regular feedback and build a collaborative, high-performance culture.
- Delivery management: Own planning, execution and delivery of application, data and AI projects. Manage scope, schedule, budget, dependencies and risks, and keep delivery on time and to the agreed quality bar.
- Hands-on engineering: Contribute to design, coding, testing and deployment, with a focus on backend services, React frontends and Azure cloud integration.
- Technical direction: Set best practices for backend engineering, React, Azure, data platforms and AI solutions so that what the team ships is robust, scalable, secure and cost-efficient.
- Cross-functional collaboration: Work with product management, UX/UI design, data science, operations and business stakeholders to align delivery with business goals and customer needs.
- Code and design reviews: Run regular reviews of code, data pipelines and AI solution designs to protect quality and enforce standards.
- Process improvement: Improve engineering and delivery practices, including Agile ceremonies, CI/CD, automated testing and DataOps/MLOps, to raise productivity and predictability.
- Stakeholder communication: Give senior management and clients clear updates on status, risks, trade-offs and technical challenges, in business terms.
Data & AI Expectations
The manager is accountable for delivering data and AI solutions alongside application work, and for making sure the team can do it responsibly and at production scale.
- Data platform delivery: Lead delivery of data solutions on Azure and a modern lakehouse stack (for example Databricks, Delta Lake, Azure Data Factory, Synapse or Fabric), including ingestion, transformation, data modeling and consumption layers.
- Data quality and governance: Ensure pipelines meet standards for data quality, lineage, security, access control and compliance, and that data products have clear owners and SLAs.
- AI and ML delivery: Guide the team from use case to production for machine learning and GenAI solutions, covering model selection, evaluation, deployment, monitoring and retraining.
- GenAI and agentic applications: Oversee delivery of LLM-based features such as retrieval-augmented generation, copilots and agent workflows, built on services like Azure OpenAI, with guardrails for accuracy, safety, latency and cost.
- MLOps and DataOps: Establish CI/CD, testing, observability and release practices for data pipelines and models, so they are managed like any other production software.
- Responsible AI: Apply practices for privacy, security, bias, explainability and human oversight, and align with company and client policies.
- Business value: Work with product and business owners to prioritize AI and data use cases by value and feasibility, define success metrics, and report outcomes rather than activity.
- Team capability: Build the team's skills in data engineering, AI engineering and AI-assisted development (for example GitHub Copilot), and set expectations for using them to raise productivity and quality.
Required Skills and Qualifications
Technical expertise
- Extensive experience in backend development with Python or another modern enterprise language (such as Java or C#), including API frameworks, microservice patterns and related tooling.
- Proficiency in React frontend development, including state management (for example Redux).
- Strong experience with Microsoft Azure, including Azure DevOps, Azure Functions, App Service and other core cloud services.
- Working knowledge of data engineering and analytics on Azure: SQL, Python, ETL/ELT pipelines, and a lakehouse or data warehouse platform such as Databricks, Synapse or Fabric.
- Practical understanding of the ML and GenAI lifecycle, including LLM-based application patterns (RAG, prompt engineering, evaluation), model deployment and monitoring, and the cloud services that support them, such as Azure OpenAI and Azure Machine Learning.
Leadership and management
- Proven experience leading engineering teams and delivering successful software projects, ideally including data or AI initiatives.
- Ability to mentor and develop engineers at all levels and to build a collaborative, high-performance team culture.
Delivery and project management
- Experience with Agile/Scrum methods and managing work in Jira or Azure DevOps.
- Track record of managing scope, budget, risk and dependencies across multiple concurrent projects, including client-facing delivery.
- Strong problem-solving skills and the ability to manage competing priorities in a fast-paced environment.
Communication
- Excellent verbal and written communication, with the ability to explain complex technical, data and AI concepts to non-technical stakeholders.
Education
- Bachelor's degree in Computer Science, Engineering or a related field. A Master's degree or relevant certifications (for example Azure, Databricks or AI-related) are a plus.
Preferred Qualifications
- Experience with microservices architecture and containerization (Docker, Kubernetes).
- Knowledge of CI/CD pipelines and automated testing frameworks.
- Hands-on experience with Databricks, Delta Lake, Spark or similar big data technologies.
- Experience delivering agentic AI or GenAI solutions in production, including evaluation, observability and cost management.
- Familiarity with MLOps tooling (for example MLflow) and with data governance and responsible AI frameworks.
- Experience in a regulated or data-intensive domain such as life sciences, healthcare, supply chain or financial services.
- Experience with other frontend frameworks (for example Angular or Vue.js) and backend technologies (for example Node.js or Java).
