Kanerika Inc.
Website:
kanerika.com
Job details:
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 release management.
• Exposure to cloud platforms (Azure preferred) and modern SaaS architecture patterns.
• Hands-on experience integrating LLMs, AI agents, or conversational AI capabilities into enterprise SaaS products.
Mindset & Soft Skills
• A structured thinker who brings process and clarity to ambiguous, fast-moving development environments.
• A genuine problem solver — someone who enjoys untangling complexity and finds clean, well reasoned solutions.
• Eager to learn — intellectually curious and excited about staying current with emerging technologies, especially in AI.
• Clear communicator who can translate technical decisions into language that resonates with both engineers and product leadership. Leads by example — sets the engineering standard through the quality of their own thinking, not just
Good to have
• Familiarity with Microsoft Fabric, Azure OpenAI, or similar AI/data platform services.
• Experience with cost optimisation strategies specific to AI inference — token management, model routing, caching, and latency reduction.
• Knowledge of observability and monitoring practices for AI-powered applications.
• Exposure to data analytics, BI, or agentic AI product development.
What you will Build
Your primary focus will be leading the development of Kanerika's AI products — solutions built around enterprise data analytics, conversational AI, and intelligent automation. You will work on products that enable natural language querying of complex enterprise data, surface insights in seconds, and deliver governed, source-traced answers across diverse data sources. You will shape how these products are architected, how AI capabilities are integrated responsibly and efficiently, and how the engineering team delivers with consistency and quality as the portfolio evolves. Education • Bachelor’s Degree in Computer Science, Engineering, Information Systems, or related field.
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