Head of Engineering - AI Startup -1.2 to 1.6 Crore

Talentiser Pvt. Ltd.; Bengaluru; On-site; INR 6,000,000 - 12,000,000; Full time

Job summary

Talentiser Pvt. Ltd. in Bengaluru is hiring a hands-on Head of Engineering to lead the design, development, and deployment of next‑generation AI‑powered products.

You will translate business vision into scalable technology, mentor high‑performing teams, and apply AI tools across the SDLC to accelerate product development and improve quality. The role requires a leader who can balance strategic tech decisions with hands‑on execution in a fast‑paced startup.

Qualifications

• 10+ years of software/tech development leadership experience.
• Strong software fundamentals with expertise in AI/ML and Generative AI.
• Experience delivering AI-powered, production-grade software.
• Practical experience with LLM integration, RAG, embeddings, and vector databases.
• Proven ability to design scalable cloud-native architectures and modern backend systems.
• Hands-on with Docker, Kubernetes, CI/CD, automated testing, and production monitoring.
• Experience leading AI-assisted SDLC and engineering productivity initiatives.
• Excellent communication and collaboration with founders and product leaders.

Responsibilities

• Own end-to-end engineering roadmap, architecture, and maintenance of AI-powered products.
• Translate business vision into technical strategy and execution plans.
• Establish engineering best practices, coding standards, and quality benchmarks.
• Drive rapid product development, prototyping, and iterative releases in a startup.
• Lead AI-powered applications using LLMs, Generative AI, and AI agents.
• Design reliable AI architectures with model integration, orchestration, monitoring, and optimization.
• Define prompt engineering approaches, context management, and inference optimization.
• Ensure AI solutions meet accuracy, latency, scalability, cost, security, and reliability.

Skills

Python

Java

TypeScript

Go

LLM integration

RAG architectures

Embeddings

Vector databases

Prompt engineering

AI engineering

Docker

Kubernetes

CI/CD

Observability

Tools

OpenAI

Anthropic

LLM platforms

Vector databases

Docker

Kubernetes

Job description

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.
• 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.
• 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.