AI Product Development Manager

Role Overview

We are looking for a Development Manager who will bring structure, rigour, and technical depth to how we build our AI products. This is a role for someone who is as comfortable deep in system design and architecture decisions as they are running a sprint, reviewing code, and shaping engineering culture. You will lead the development of Kanerika’s AI products — solutions at the intersection of data analytics and enterprise software — working alongside a team of intelligent, high-energy engineers who set a high bar for themselves. We want a builder-leader — someone who brings structured thinking to product development, keeps the team sharp and focused, and is genuinely excited about where AI is taking enterprise software.

Key Responsibilities

Structured Product Development

• Establish and drive a structured, repeatable approach to product development across Kanerika’s AI product portfolio — covering planning, architecture, sprint execution, and release discipline.

• Own the end-to-end development lifecycle: from requirements and system design through build, test, deployment, and continuous improvement.

• Introduce and enforce engineering best practices — code quality, documentation standards, design reviews, and testing frameworks — that scale with the team and the products.

• Bring clarity and predictability to delivery without sacrificing the team’s pace and engineering ambition. Technical Leadership & System Design

• Lead system design and architecture decisions for AI-powered product features — ensuring solutions are scalable, cost-efficient, and built for the long term.

• Drive cost optimisation strategies for AI features, including model selection, inference efficiency, caching strategies, and smart resource allocation.

• Conduct meaningful code and design reviews — setting a high technical bar while helping the team grow through constructive, experience-backed feedback.

• Identify technical risks early and make clear, well-reasoned calls on trade-offs across build quality, speed, and scalability. AI-Savvy Development Practices

• Champion best practices for AI-based product development — including responsible LLM integration, prompt engineering governance, hallucination mitigation, evaluation frameworks, and AI observability.

• Guide the team in making pragmatic decisions around AI adoption: when to build, when to buy, and how to integrate third-party AI capabilities cleanly into product architecture.

• Stay ahead of evolving AI tooling, frameworks, and patterns — bringing relevant ideas and approaches back to the team proactively. Team & Delivery

• Lead a team of intelligent and highly capable engineers — your role is to amplify their output, not manage their time.

• Run effective sprint planning, retrospectives, and delivery cycles — keeping the team focused, unblocked, and aligned to product priorities.

• Work closely with the Product Leader to translate the product roadmap into well-scoped, executable development plans; provide regular progress updates and proactively surface risk factors, dependencies, and blockers before they impact delivery.

• Foster a culture of ownership, curiosity, and continuous learning — where engineers feel challenged and take pride in what they ship.

Required Qualifications Experience

Technical Skills

• 10–12 years of overall experience in software or product development, with a strong hands-on engineering background throughout.

• At least 3 years of experience leading a development team — with a track record of shipping well engineered, production-grade software.

• Demonstrated experience building structured development processes in fast-paced, product-driven environments.

• Prior experience working on or alongside AI-integrated products — whether LLM-based features, ML pipelines, or intelligent automation — is an advantage.

• Strong hands-on proficiency in Java for backend development; working knowledge of Python, particularly for AI/ML integration and data processing workflows.

• Solid understanding of system design principles — including distributed systems, API design, data modelling, and scalability patterns.

• Good hands-on experience with AI/LLM integration patterns, prompt engineering, cost optimisation in AI workloads, and responsible AI practices in product development.

• Good understanding of DevOps best practices — including CI/CD pipelines, version control workflows, containerisation, automated testing, and …