Location : Bengaluru, India - Hybrid (3 days/week in office)

Reporting to : CEO

Experience : Preferably 10 - 15 years of enterprise software engineering experience. Candidates with fewer years may be considered if they demonstrate exceptional hands-on engineering and leadership expertise.

About the Role :

We are looking for a senior technology leader to drive the organisation's product, AI and platform technology strategy while leading engineering excellence across AI/ML, data, platform engineering, DevOps, infrastructure and quality engineering.

This is a mission-critical leadership role requiring someone who combines strong hands-on technical depth with strategic engineering leadership and has experience building and scaling enterprise-grade B2B SaaS products.

The ideal candidate should have successfully taken AI/ML or GenAI solutions from early-stage prototypes to scalable production platforms, particularly involving AI agents, multi-agent orchestration, intelligent automation, complex workflows and document intelligence.

Key Responsibilities :

Technology & Product Leadership :

- Define and execute the overall technology vision, architecture and long-term platform roadmap.

- Lead the architecture, design and delivery of highly scalable enterprise systems.

- Drive engineering excellence, development velocity, scalability, security and reliability.

- Partner with executive leadership on business strategy, customer requirements and product delivery.

Engineering & Platform Leadership :

- Lead engineering functions across product technology, platform engineering, AI/ML, DevOps, infrastructure and Quality Engineering.

- Architect and scale cloud-native platforms across Azure, AWS and GCP.

- Drive distributed systems and microservices architecture.

- Own production reliability, uptime, engineering efficiency and cloud cost optimisation.

- Build, mentor and scale high-performing full-stack and machine-learning engineering teams.

GenAI & AI Strategy :

- Build scalable, production-grade GenAI, ML and enterprise data capabilities.

- Own GenAI/LLM solution architecture.

- Architect solutions involving AI agents, autonomous workflows and multi-agent orchestration.

- Lead the development of intelligent automation, data extraction and decision-intelligence solutions.

- Provide hands-on architectural oversight for LLMs, applied AI and deep-learning products.

- Build sophisticated document intelligence and intelligent document automation platforms, including solutions comparable to patent parsing, legal workflow automation and structured decision systems.

What Success Looks Like :

- AI Transformation : Build enterprise-grade AI capabilities leveraging GenAI, LLMs, ML, AI agents and data engineering.

- Platform Scaling : Architect and scale the technology platform to support enterprise customers and global growth.

- Engineering Culture : Build a high-performance engineering organisation focused on ownership, innovation, execution and continuous learning.

Required Experience & Qualifications :

- Preferably 10 - 15 years of experience in enterprise software engineering, B2B SaaS or technology-first product companies.

- Strong experience in engineering leadership and technology strategy.

- Proven experience taking AI/ML or GenAI prototypes into production-grade enterprise SaaS platforms.

- Hands-on experience with LLMs, AI agents, multi-agent systems and workflow orchestration.

- Experience with document intelligence, unstructured data processing or intelligent automation is highly desirable.

- Experience building and mentoring teams of full-stack, platform and ML/AI engineers.

- Proven experience defining and executing technology strategy, architecture and platform roadmaps.

- Strong cloud-native engineering expertise across Azure, AWS and/or GCP.

- Experience managing product delivery end-to-end, including production operations and customer support.

Technical Skills :

- Programming : Java, Python

- Frontend/Backend : JavaScript, React, Angular, Node.js

- AI : GenAI, LLMs, AI Agents, Applied AI, Deep Learning

- AI Architecture : Multi-Agent Orchestration, Autonomous Workflows, Decision Intelligence

- Data : SQL, NoSQL, Data Modelling, Unstructured Data

- Architecture : Distributed Systems, Microservices, Platform Architecture

- Cloud : Azure, AWS, GCP

- Engineering : DevOps, CI/CD, Git, Quality Engineering

- Methodologies : Agile

- Tools : Atlassian ecosystem

Leadership Competencies :

- Strong engineering and technology leadership.

- Ability to remain technically hands-on while leading at a strategic level.

- Strong product and delivery management capabilities.

- Experience working directly with senior executives and enterprise customers.

- Excellent stakeholder communication and influencing skills.

- Strong business acumen combined with deep technical expertise.

- Strong analytical, problem-solving and decision-making capabilities.