AI Lead Developer

We're looking for an AI Lead to own and grow our combined AI/ML and Data Engineering function. This is a hands-on leadership role for someone who has built a career in AI/ML or Data Science, and who also understands the data infrastructure that makes AI work in the real world. You won't just be setting AI strategy in the abstract; you'll be leading the teams that build the models and the teams that build the pipelines feeding those models. The ideal person is a strong AI/ML or Data Science leader with genuine, practical data engineering knowledge: someone the data engineering team will respect because you understand their world, not just direct it.

Responsibilities:

• Lead, mentor, and grow two connected teams, AI/ML and Data Engineering, setting direction, priorities, and standards for both.
• Define and drive the AI roadmap, aligning it with business goals and making sure AI initiatives ship to production and deliver measurable impact.
• Oversee the design of robust, scalable data pipelines and infrastructure that power AI/ML systems, ensuring data quality, reliability, and performance.
• Take AI/ML solutions from concept to production, including model development, deployment, monitoring, and retraining (MLOps).
• Partner with product, engineering, and business stakeholders to identify high-value AI opportunities and translate them into delivered solutions.
• Set technical standards and best practices across both teams, balancing innovation speed with maintainability and governance.
• Communicate complex AI and data concepts clearly to both technical teams and senior, non-technical stakeholders.
• Stay current with developments in AI/ML (including Generative AI and LLMs) and data engineering, and bring the relevant ones into the organisation.

Requirements:

• 9-13 years of experience in AI/ML, Data Science, or a closely related field, with a track record of shipping AI/ML solutions to production.
• Proven people-leadership experience, you've managed and grown technical teams, not just individual contributors.
• Strong hands-on or recent practical knowledge of data engineering: data pipelines, ETL, and big-data tooling (e. g., Spark, Hadoop, SQL/NoSQL, cloud data warehouses).
• Enough depth to credibly lead a data engineering team.
• Solid grounding in core ML supervised/unsupervised methods, modern frameworks, and putting models into production (MLOps, CI/CD, cloud).
• Ability to bridge technical and business worlds and communicate effectively with both.
• Bachelor's or higher in Computer Science, Engineering, Statistics, or a related quantitative field.

Nice to have:

• Hands-on experience with Generative AI / LLMs in production settings.
• Domain experience in [your industry, e. g., fintech / BFSI / e-commerce].
• Experience working with cloud platforms (AWS / GCP / Azure).
• Experience setting up MLOps, monitoring, and model-retraining frameworks from scratch.