Head of Engineering - AI Startup -1.2 to 1.6 Crore

Head of Engineering – AI Startup

Experience: 10+ Years

Employment Type: Full-time

1. About the Role

We are looking for a hands-on, technology-driven Head of Engineering to lead the design, development, and deployment of next-generation AI-powered products. The ideal candidate will combine strong software engineering fundamentals with expertise in AI/ML, Generative AI, LLM-based applications, and modern cloud-native architectures.

This role requires a leader who can translate business vision into scalable technology, build and mentor high-performing engineering teams, and leverage AI tools across the software development lifecycle (AI-led SDLC) to accelerate product development, improve engineering productivity, and maintain high standards of quality and reliability.

The ideal candidate should be comfortable operating in a fast-paced startup environment, balancing strategic technology decisions with hands-on technical execution.

2. Key Responsibilities

A. Engineering Leadership & Product Development

• Own the end-to-end engineering roadmap, architecture, development, testing, deployment, and maintenance of the company's AI-powered products.
• Translate business requirements and product vision into technical strategy, engineering priorities, and execution plans.
• Establish engineering best practices, coding standards, design principles, and quality benchmarks.
• Drive rapid product development, prototyping, experimentation, and iterative releases in a startup environment.
• Work closely with founders, product managers, and business stakeholders to align engineering priorities with business outcomes.

B. AI Engineering & Generative AI

• Lead the development and deployment of AI-powered applications using LLMs, Generative AI, AI agents, and other relevant AI technologies.
• Build production-grade AI systems using technologies such as OpenAI, Anthropic, open-source LLMs, RAG, vector databases, and agentic workflows, as relevant to the product.
• Design reliable AI application architectures, including model integration, orchestration, evaluation, monitoring, and optimization.
• Establish approaches for prompt engineering, context management, model selection, inference optimization, and AI application performance.
• Ensure AI solutions meet requirements for accuracy, latency, scalability, cost efficiency, security, and reliability.
• Collaborate with data science and ML teams, where applicable, to operationalize models and integrate them into customer-facing products.

C. AI-Led Software Development Lifecycle (SDLC)

• Drive the adoption of AI-assisted engineering across requirements analysis, solution design, coding, testing, debugging, code reviews, documentation, and deployment.
• Implement AI coding tools such as GitHub Copilot, Cursor, Claude Code, or equivalent platforms.
• Establish workflows for AI-generated code validation, automated testing, security checks, and human review.
• Improve engineering velocity and developer productivity through AI-driven automation and intelligent development workflows.
• Define and track metrics such as development cycle time, deployment frequency, defect rates, code quality, and engineering throughput.

D. Architecture, Scalability & Cloud Infrastructure

• Design and build scalable, secure, and maintainable software architectures using microservices or modular architectures, APIs, event-driven systems, and distributed computing where appropriate.
• Lead cloud infrastructure and deployment strategy across platforms such as AWS, Azure, or Google Cloud.
• Establish CI/CD pipelines, infrastructure automation, observability, monitoring, and incident management practices.
• Ensure application performance, availability, data protection, and cost optimization as the product and customer base scale.
• Make pragmatic build-versus-buy and technology selection decisions.

E. Team Building & People Leadership

• Build, lead, and mentor a high-performing engineering team across software engineering, AI engineering, platform engineering, and quality engineering.
• Recruit strong engineering talent and establish clear ownership, accountability, and performance expectations.
• Create a culture of technical excellence, rapid experimentation, continuous learning, and customer-centric execution.
• Introduce effective engineering processes without compromising startup agility.
• Partner with leadership on hiring plans, resource allocation, organizational design, and engineering budgets.

F. Security, Quality & Responsible AI

• Establish robust software testing, code quality, application security, and release management practices.
• Implement safeguards for AI applications, including data privacy, prompt injection risks, model evaluation, output validation, and access controls.
• Ensure responsible handling of customer data and compliance with applicable regulatory requirements.
• Build systems that are observable, testable, resilient, and ready for production workloads.

3. Required Skills & Experience

• 10+ years of experience in software engineering, product engineering, or technology development, with meaningful experience leading engineering teams or technical projects.
• Strong software development expertise in one or more languages such as Python, Java, TypeScript, Go, or similar.
• Demonstrated experience designing and delivering scalable, production-grade software products.
• Practical experience building applications powered by Generative AI, LLMs, AI agents, or machine learning.
• Strong understanding of LLM integration, RAG architectures, embeddings, vector databases, prompt engineering, and AI application evaluation.
• Experience with modern backend frameworks, APIs, databases, distributed systems, and cloud infrastructure.
• Working knowledge of Docker, Kubernetes, CI/CD, automated testing, and production monitoring.
• Experience with AI-assisted software development tools and modern engineering productivity practices.
• Strong architectural judgment, problem-solving skills, and the ability to make sound technical trade-offs.
• Proven ability to work in an agile, fast-paced environment with evolving requirements.
• Excellent communication skills and the ability to collaborate with founders, product leaders, and business stakeholders.

4. Preferred Qualifications

• Experience in an early-stage or growth-stage AI/product startup.
• Experience taking an AI product from prototype or MVP to production and scaling it to real customers.
• Hands-on experience with agentic AI systems, tool calling, workflow orchestration, and multi-step AI applications.
• Experience with LLM evaluation frameworks, observability, model gateways, and inference cost optimization.
• Experience implementing AI-driven testing, code generation, and automated engineering workflows.
• Exposure to MLOps, model serving, fine-tuning, data pipelines, or GPU infrastructure, where relevant.
• A track record of building engineering teams and delivering products under tight timelines.