Head of Engineering – Agentic AI

Reports to: Executive Management
Role Level: Senior Leadership
Department: Engineering & Product Development
Location: Bangalore / Hyderabad / India Remote
Job Specification: Head of Engineering – Agentic AI
About the Role
We are looking for a highly capable, hands-on engineering leader to drive the technical vision, architecture, and engineering execution of our evolving product ecosystem.
This is a key leadership role within a fast-paced, AI-driven startup environment. The successful candidate will combine deep technical expertise with strategic thinking, taking ownership of engineering direction while remaining actively involved in architecture, development, and critical technical decisions.
The ideal candidate will be passionate about emerging AI technologies, continuously explore new tools and approaches, and understand how to translate AI capabilities into scalable, secure, and commercially valuable product features.
Reporting directly to executive management, this individual will play a pivotal role in shaping the technology roadmap, establishing engineering best practices, guiding the development team, and ensuring that our products are built on robust, scalable, and future- ready foundations.
Key Responsibilities
1. Technical Strategy & Engineering Leadership
Define and drive the technical vision, engineering strategy, and architecture roadmap in alignment with business objectives.
Provide technical direction for product development, ensuring solutions are scalable, maintainable, secure, and fit for purpose.
Work closely with executive management and product stakeholders to translate business requirements into technical solutions.
Evaluate emerging technologies, frameworks, and architectural approaches to maintain a competitive technical advantage.
Take ownership of critical technical decisions and proactively identify opportunities for improvement.
2. AI Strategy & Innovation
Stay hands-on and up to date with the rapidly evolving AI landscape, including generative AI, large language models (LLMs), AI agents, retrieval-augmented generation (RAG), and AI-powered automation.
Identify opportunities to integrate AI capabilities into existing and new products.
Assess AI tools, models, frameworks, and platforms based on their business value,
technical suitability, cost, and scalability.
Lead the design and implementation of production-ready AI-powered features and
services.
Establish best practices for AI integration, evaluation, reliability, security, and
ongoing performance optimisation.
3. Hands-on Architecture & Development
Operate at a hands-on architect level, actively contributing to solution design, code development, code reviews, and technical problem-solving.
Design and implement backend services, APIs, microservices, and distributed systems using Node.js and/or Python.
Establish architectural standards, coding practices, design patterns, and engineering guidelines.
Review complex technical challenges and guide the team towards effective, pragmatic solutions.
Balance rapid product delivery with long-term maintainability, reliability, and technical quality.
4. Cloud Infrastructure & DevOps
Lead the design, implementation, and optimisation of cloud-native solutions, particularly within Amazon Web Services (AWS).
Demonstrate extensive practical experience with AWS services, especially AWS Lambda and container orchestration technologies such as Amazon EKS or ECS.
Design and manage scalable, resilient, and cost-efficient cloud infrastructure.
Drive containerisation, Kubernetes adoption where appropriate, CI/CD automation,
infrastructure as code, and deployment best practices.
Establish effective monitoring, logging, observability, security, and incident
management practices.
Ensure systems meet appropriate standards for availability, performance, data
protection, and operational resilience.
5. System Architecture & Technical Governance
Design end-to-end system architectures that support current product needs and future business growth.
Make informed decisions around service boundaries, data architecture, integration patterns, APIs, and infrastructure.
Identify architectural risks, scalability bottlenecks, technical debt, and opportunities for simplification.
Establish appropriate engineering standards and technical governance without creating unnecessary bureaucracy.
Ensure architectural decisions support rapid iteration, reliability, and sustainable product development.
6. Team Leadership & Development
Lead, mentor, and develop engineering team members, fostering a culture of ownership, accountability, collaboration, and continuous learning.
Provide clear technical direction while empowering engineers to make informed decisions.
Establish effective engineering workflows, delivery standards, code review practices, and quality assurance processes.
Support recruitment, technical assessments, onboarding, and the development of engineering capabilities as the organisation grows.
Build a high-performing engineering culture focused on outcomes, technical excellence, and continuous improvement.
7. Ownership & Execution
Take end-to-end ownership of engineering outcomes, from initial technical concept through implementation, deployment, and ongoing optimisation.
Work proactively, identify what needs to be done, and drive initiatives forward without waiting for detailed instructions.
Anticipate technical and delivery risks and address them before they become critical issues.
Make pragmatic decisions that balance speed, quality, cost, and business priorities.
Communicate technical progress, risks, dependencies, and recommendations clearly
to executive management.
Essential Skills & Experience
Significant experience in software engineering, system architecture, and technical leadership, with a track record of delivering production-grade software systems.
Strong hands-on programming experience in Node.js and/or Python.
Extensive practical experience with AWS cloud services, particularly AWS Lambda
and cloud-native application architecture.
Strong experience with Docker and Kubernetes, including production deployment
and orchestration; experience with Amazon EKS or ECS is highly desirable.
Proven ability to design scalable, secure, resilient, and maintainable distributed
systems.
Strong understanding of APIs, microservices, asynchronous processing, event-driven
architecture, databases, and system integration.
Practical knowledge of CI/CD, infrastructure as code, automated testing, monitoring,
and production operations.
Demonstrable experience evaluating and integrating AI technologies into software
products.
Experience leading engineering teams and influencing technical decisions across
product and business functions.
Excellent problem-solving, communication, prioritisation, and decision-making skills.
A strong sense of accountability, initiative, and ownership.
Desirable Experience
Experience working in a startup, scale-up, or fast-moving product-led organisation.
Experience building AI-enabled products using LLM APIs, RAG pipelines, vector
databases, AI agents, or model orchestration frameworks.
Familiarity with AI evaluation, model observability, prompt management, and
responsible AI practices.
Experience with serverless and event-driven architectures, including AWS Lambda,
API Gateway, SQS, SNS, and EventBridge.
Experience with infrastructure automation tools such as Terraform, AWS CDK, or
CloudFormation.
Knowledge of cloud security, identity and access management, secrets management,
and cost optimisation.
Experience scaling engineering teams, improving delivery processes, and managing
technical debt.
Leadership Attributes
The successful candidate will be:
Technically hands-on: Comfortable moving between strategic architecture discussions and writing or reviewing production code.
AI-forward: Curious, experimental, and proactive in understanding and applying the latest relevant AI technologies.
An owner, not just an executor: Takes responsibility for outcomes and drives initiatives independently.
Strategic and pragmatic: Connects technology decisions to business priorities and commercial outcomes.
A builder: Enjoys creating products, solving difficult problems, and establishing strong engineering foundations.
A decisive leader: Makes informed decisions, establishes clear direction, and takes responsibility for the results.
A strong communicator: Can explain complex technical concepts and trade-offs to engineers, product teams, and executive stakeholders.
Adaptable: Thrives in an e

Work arrangement
Hybrid