Responsibilities

Lead the end to end technical design of AI and Agentic AI solutions, ensuring alignment with enterprise architecture, security controls, and platform standards.
Translate high level requirements from architects and product owners into detailed technical specifications, development tasks, and implementation plans.
Oversee the full development lifecycle for AI, software, and automation workflows, providing hands on guidance and removing technical blockers for the team.
Provide technical leadership across backend, frontend, API integration, LLM orchestration, agent frameworks, and workflow automation to ensure consistency and quality.
Review and approve solution designs, architecture diagrams, data flows, and code contributions from senior and junior engineers.
Drive the integration of AI solutions with enterprise systems, ensuring robust connectivity through APIs, MCP and other frameworks, while enforcing best practices.
Guide the design of hybrid solutions that combine AI models, prompting logic, rule engines, middleware, RPA, and human in the loop elements in a coherent workflow.
Oversee the stability and performance of production systems, define monitoring strategies, and ensure issues are escalated and resolved quickly.
Establish engineering standards covering code quality, documentation, testing strategies, CI and CD practices, and security reviews.
Mentor and coach senior and junior AI engineers, provide structured guidance, and drive upskilling for full stack development, AI integration, and delivery discipline.
Work closely with platform teams, cybersecurity, data engineering, product owners, and business stakeholders to ensure alignment and timely delivery.
Own risk identification and mitigation across AI solutions, including fallback strategies, model behaviour oversight, and protection against misuse or unexpected outputs.
Represent the AI engineering team in architecture discussions, solution reviews, and governance forums.
Able to design and maintain RAG systems, including content ingestion, embedding, vector storage, retrieval tuning, and accuracy checks for enterprise use cases.
Drive continuous improvement for the marketplace recommender engine, enterprise AI services, and core reusable components.
Coordinate effort estimation, sprint planning, prioritisation, and resource allocation for the AI engineering team.
Ensure delivered solutions are secure, scalable, compliant, and ready for production deployment.
Keeping close watch on new developments in AI, running small proof of concept work to understand their value, and advising the team on which solutions are suitable for adoption.

Requirements

Bachelor’s or Master’s degree in Computer Science, Engineering, Artificial Intelligence or a related technical field. Candidates with advanced specialisation in full stack engineering or AI engineering are preferred.
More than 7 years of hands on experience delivering full stack software and AI solutions, with at least 2 to 3 years in a senior or technical lead capacity supervising engineers. Proven experience designing end to end AI and Agentic AI solutions.
Strong full stack engineering capability across frontend, backend, API design and system integration, with deep proficiency in Python, TypeScript or NodeJS and SQL.
Experienced in designing and leading implementation of LLM based and agentic AI systems, including orchestration patterns using frameworks such as LangChain or LangGraph.
Strong capability in integrating AI solutions with enterprise platforms, covering RESTful APIs, gRPC, MCP and secure middleware components.
Solid understanding of authentication and authorization patterns including OAuth2, JWT and SSO technologies.
Hands on experience designing scalable cloud deployments on Azure, AWS or GCP with good understanding of CI and CD practices.
Proven ability to conduct technical reviews, enforce engineering standards, and guide other engineers in code quality, architecture and system performance.
Proven ability to troubleshoot complex issues across frontend, backend, APIs, AI models and middleware in production environments.
Experience in LLMOps/GenAIOps, MLOps, DevSecOps
Passion for applied R&D, staying ahead of AI advancements, and bringing innovative ideas into production.
Strong problem-solving ability, attention to detail, and ability to collaborate effectively across teams.
Capable to work in a cross-functional team (Software/AI engineers, data platform, business users).
Experience in building secure, enterprise-grade integrations.