- Key Accountabilities
- Lead the AI Engineering team responsible for building AI-powered agents, copilots, and intelligent automation solutions across enterprise products and platforms.

- Own end-to-end engineering delivery for AI initiatives, including planning, execution, quality, release readiness, and operational stability.

- Work closely with product management, architecture, and business stakeholders to convert AI use cases and product goals into executable engineering plans.

- Build and scale a high-performing AI Engineering team with strong ownership, engineering discipline, collaboration, and execution focus.

- Drive sprint planning, prioritization, estimation, dependency management, and delivery governance for AI-related initiatives.

- Ensure AI solutions are developed with the required standards of scalability, performance, reliability, security, observability, and maintainability.

- Partner with the AI Architect to ensure that engineering implementation aligns with defined solution architecture, design standards, and long-term technology direction.

- Provide technical leadership to the team by guiding implementation approaches, engineering best practices, code quality, testing discipline, and production readiness.

- Establish development processes, coding standards, review mechanisms, DevOps practices, and quality controls for AI applications and platforms.

- Collaborate with platform, DevOps, QA, security, and product engineering teams to ensure smooth integration and deployment of AI capabilities into enterprise products.

- Drive execution of AI use cases such as intelligent assistants, recommendation engines, knowledge copilots, reporting assistants, support automation, and workflow intelligence solutions.

- Monitor engineering progress, identify execution risks, remove blockers, and ensure timely delivery of committed outcomes.

- Own team capacity planning, resource allocation, skill development, performance management, and hiring for the AI Engineering team.

- Define productivity, quality,



and operational metrics for the team and drive continuous improvement across delivery and engineering practices.

- Foster a culture of innovation, accountability, experimentation, and disciplined execution within the AI Engineering team.

- Ensure compliance with enterprise standards related to security, privacy, responsible AI usage, access control, auditability, and governance.

- Support production rollouts, issue resolution, incident management, and continuous improvement of AI capabilities in live environments.

- Stay updated on industry trends, AI engineering practices, and emerging technologies, and drive relevant adoption in alignment with product and engineering goals.

- Mandatory Skills
- Bachelor’s degree in Computer Science, Information Technology, Artificial Intelligence, Data Science, Engineering, or a related field.

- Minimum of 10 years of experience in software engineering, with at least 3 to 5 years of experience leading engineering teams in product development environments.

- Relevant hands-on experience in AI/ML, Generative AI, LLM-based applications, or intelligent automation platforms.

- Proven experience in managing engineering teams delivering enterprise-scale software products and complex technology initiatives.

- Strong programming and technical understanding in areas such as Python, backend development, APIs, microservices, distributed systems, and cloud-native applications.

- Good understanding of Large Language Models, prompt engineering, embeddings, semantic search, vector databases, retrieval-augmented generation, and agent-based application development.

- Strong experience in engineering delivery management, Agile execution, sprint planning, estimation, release management, and cross-functional coordination.





- Ability to translate business and product requirements into structured engineering plans, delivery roadmaps, and execution milestones.

- Experience in building high-performing teams through effective hiring, mentoring, coaching, and performance management.

- Strong understanding of software quality practices, code reviews, testing strategies, CI/CD pipelines, monitoring, observability, and production support models.

- Good knowledge of cloud platforms such as AWS, Azure, or GCP and modern deployment practices for enterprise applications.

- Strong stakeholder management, communication, and collaboration skills with the ability to work with product, architecture, engineering, and business teams.

- Ability to manage technical risk, delivery challenges, and operational issues in a structured and proactive manner.

- Knowledge of Agile methodologies and contemporary product engineering practices.

- Desirable Skills
- Experience in enterprise software, SaaS platforms, customer engagement platforms, analytics products, or digital transformation programs.

- Experience leading teams building AI assistants, copilots, chatbots, or agentic applications for enterprise use cases.

- Familiarity with AI orchestration and agent development frameworks such as LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar tools.

- Exposure to Java, Spring Boot, Node.js, Kafka, workflow engines, and event-driven enterprise platforms.

- Familiarity with vector stores, caching layers, LLMOps tools, AI observability platforms, and model evaluation practices.

- Understanding of recommendation systems, analytics platforms, personalization engines, and decisioning systems.

- Experience driving engineering governance in areas such as secure development, privacy, compliance, responsible AI, and enterprise controls.

- Experience working in collaborative and cross-functional global product engineering teams.

- Understanding of end-to-end enterprise AI platform engineering and production deployment considerations.

📌 Head of Mobilytix Modernization (Gurugram)
🏢 Comviva
📍 Gurugram