Head of Engineering AI & SaaS Products

Role Type: Full time | Senior Technology Leadership

Experience: 15+ years in software engineering, with at least 5 years in technical leadership, architecture, or engineering management

Industry Experience: AI- Native & AI-powered SaaS Products, Enterprise Products, SaaS Products

1. Role Overview

We are looking for an accomplished Head of Engineering – AI & SaaS Products to lead the architecture, engineering, development, and delivery of multiple next-generation software products, including AI-native SaaS applications.

This role requires a rare combination of deep technical expertise, hands-on engineering capabilities, architectural decision-making, AI innovation, and strong team leadership. The individual will be responsible for translating business objectives and product requirements into scalable technical solutions, selecting the right technologies, guiding development teams, and ensuring the successful delivery of secure, high-performance, market-ready products.

The ideal candidate is a technology leader who understands not only how to build software, but also what to build, why to build it, how to build it efficiently, and how to leverage AI to create a meaningful competitive advantage.

This individual will establish engineering standards, improve development processes, mentor developers, and create a culture of technical excellence, accountability, innovation, and continuous improvement.

2. Key Responsibilities

A. Technical Leadership & Engineering Strategy

- Define engineering strategy, technical vision, and development standards across products.
- Translate business requirements into scalable, secure, and maintainable solutions.
- Make technology, architecture, and build-versus-buy decisions based on business needs, cost, and scalability.
- Establish reusable components, coding standards, reviews, testing, and release practices.
- Identify technical risks, performance issues, and modernization opportunities.

B. Software Architecture & Full-Stack Engineering

- Design end-to-end application architecture covering frontend, backend, databases, APIs, integrations, and security.
- Evaluate and select appropriate technology stacks, frameworks, and database systems.
- Build scalable, modular architectures using monoliths or microservices where appropriate.
- Define data models, API contracts, system modules, and integration patterns.
- Review architecture, technical designs, and code; remain hands-on with complex implementations and prototypes.
- Expected breadth: React/Next.js, TypeScript, Node.js, Flutter or equivalent, SQL/NoSQL, REST APIs, and event-driven systems.

C. AI Engineering & AI-Native Product Development 1. LLMs & Generative AI

- Understand LLMs, transformers, embeddings, context windows, model limitations, and model selection.
- Evaluate and integrate commercial and open-source models.
- Implement structured outputs, tool calling, streaming, and reliable AI workflows.
- Determine when to use foundation models, fine-tuning, or custom models.

2. RAG & Enterprise Knowledge Systems

- Design RAG pipelines covering document processing, OCR, chunking, embeddings, vector databases, hybrid search, and reranking.
- Understand advanced approaches such as Agentic RAG, GraphRAG, and multimodal retrieval.
- Implement source-grounded responses, citations, and hallucination-reduction mechanisms.

3. AI Agents & Agentic Workflows

- Build AI agents that use tools, execute workflows, and interact with enterprise systems.
- Understand orchestration, planning, memory, multi-agent coordination, and human oversight.
- Implement retries, verification, error recovery, and controlled autonomy.
- Understand MCP, custom tool servers, reusable agent skills,



and agent-to-agent communication.

Choose between deterministic workflows, automation, LLMs, and autonomous agents appropriately. 4. Model Adaptation & Inference

- Understand fine-tuning, LoRA/QLoRA, quantization, and model optimization.
- Possess working knowledge of pretraining, RLHF, and DPO.
- Understand model serving, GPU fundamentals, batching, caching, and inference cost optimization.
- Evaluate tools such as vLLM, Ollama, and llama.cpp when self-hosting is appropriate.

5. Multimodal AI & Automation

- Evaluate solutions involving document intelligence, vision, OCR, speech, audio, and real-time voice.
- Build AI-powered business automation using APIs, integrations, and tools such as n8n.
- Understand browser automation and computer-use agents.

6. AI Quality, Security & Governance

- Evaluate AI accuracy, retrieval quality, hallucinations, and agent reliability.
- Implement prompt-injection defenses, guardrails, access controls, and sensitive-data protection.
- Ensure tenant isolation, privacy, auditability, and secure AI workflows.
- Monitor prompts, model calls, latency, errors, token usage, and costs.
- Establish human approval for sensitive AI-driven actions.

7. AI Research & Innovation

- Track emerging AI models, frameworks, agent architectures, and development tools.
- Prototype and benchmark promising technologies.
- Evaluate AI coding tools such as Claude Code, OpenAI Codex, Cursor, and GitHub Copilot.
- Convert successful experiments into production-ready capabilities.

Expectation: The candidate need not master every technique but must understand when to use it and demonstrate hands-on experience delivering production AI features. D. Cloud Infrastructure, DevOps & Production Engineering

- Select suitable hosting and infrastructure across shared hosting, VPS, dedicated servers, and cloud platforms.
- Understand AWS, Azure, GCP, Docker, Kubernetes, CI/CD, Nginx, DNS, SSL/TLS, and networking.
- Establish development, testing, staging, and production environments.
- Implement monitoring, logging, backups, disaster recovery, rollback, and security controls.
- Optimize infrastructure utilization, reliability, and AI operating costs.
- Guide infrastructure decisions and resolve deployment challenges.

E. Product Architecture & Business Requirements

- Collaborate with leadership, product owners, business analysts, designers, and developers.
- Convert business objectives into requirements, workflows, technical specifications, and implementation plans.
- Identify requirement gaps, dependencies, constraints, risks, and unnecessary complexity.
- Define modules, integrations, data flows, and system interfaces.
- Assess feasibility, estimate effort, and align technical decisions with product strategy and user needs.

F. Engineering Execution & Project Delivery

- Convert priorities into technical roadmaps, release plans, sprints, and milestones.
- Break requirements into tasks, dependencies, and acceptance criteria.
- Allocate work based on skills, complexity, capacity, and priorities.
- Track progress, resolve blockers, manage scope changes, and mitigate delivery risks.
- Coordinate multiple products, integrations, testing, and releases.
- Communicate delivery status, risks, and forecasts to senior management.

G. Team Leadership & Developer Development

- Lead and mentor frontend, backend, full-stack,



and AI engineers.
- Build a culture of ownership, accountability, collaboration, and continuous learning.
- Guide technical decisions, resolve complex challenges, and review engineering work.
- Establish coding, documentation, knowledge-sharing, and AI-assisted development practices.
- Support recruitment, technical interviews, performance management, and upskilling.
- Manage team capacity and reduce dependency on individual team members.

H. Quality Assurance, Security & Release Excellence

- Establish testing and quality standards across development and production.
- Ensure appropriate unit, integration, API, regression, performance, and security testing.
- Introduce automated testing and CI/CD quality gates.
- Validate AI accuracy, grounding, permissions, adversarial inputs, and failure scenarios.
- Track defects, incidents, and technical debt; drive root-cause analysis and prevention.
- Ensure security, privacy, resilience, and maintainability.

I. Engineering Innovation & Operational Excellence

- Automate repetitive engineering activities and improve development workflows.
- Introduce reusable libraries, templates, common services, and architectural patterns.
- Evaluate tools and technologies based on measurable benefits.
- Improve productivity through automation, AI-assisted development, and better tooling.
- Maintain technical documentation and architectural decision records.
- Continuously improve engineering performance through measurable outcomes.

4. Preferred Technology Ecosystem

- Application Engineering: React, Next.js, TypeScript, Node.js, Express.js, Python, FastAPI, PostgreSQL, MySQL, MongoDB, Redis, and REST APIs.
- AI Engineering: OpenAI, Anthropic, Google, open-source models, LangGraph, LangChain, LlamaIndex, OpenAI Agents SDK, vector search, reranking, and knowledge graphs.
- AI Development & Monitoring: Claude Code, OpenAI Codex, Cursor, GitHub Copilot, LangSmith, and Langfuse.
- Inference: vLLM, Ollama, llama.cpp, or equivalent tools.
- Cloud & DevOps: AWS, Azure, GCP, Docker, Kubernetes, CI/CD, Nginx, monitoring, and infrastructure automation.
- Security: Authentication, authorization, RBAC, tenant isolation, encryption, audit logs, and secure API design.

Equivalent technologies are acceptable. Decisions must be based on business requirements and technical trade-offs. 6. Experience & Qualifications

Mandatory Requirements

- 15+ years of overall software engineering experience.
- At least 7–10 years in technical leadership, architecture, or engineering management.
- Proven experience designing and delivering production-grade SaaS or enterprise applications.
- Hands-on experience integrating LLMs, implementing RAG, and building agentic or tool-using AI workflows.
- Strong expertise in system architecture, technology selection, databases, APIs, cloud infrastructure, and security.
- Experience leading engineering teams, reviewing technical work, resolving blockers, and delivering multiple projects.
- Ability to remain hands-on with architecture, prototypes, and complex engineering challenges.
- Strong analytical, communication, decision-making, and stakeholder-management skills.

Preferred Qualifications

- Experience building AI-native SaaS products from architecture through production.
- Exposure to HR tech, recruitment, procurement, ERP/CRM integrations, or enterprise workflows.
- Experience with multi-tenant SaaS, enterprise security, model fine-tuning, or self-hosted AI.
- Experience establishing engineering teams, standards, CI/CD, and scalable delivery processes.
- Proven ability to turn emerging AI technologies into commercially valuable products.
- Relevant bachelor's or master's degree, or equivalent practical experience.

📌 Head of Engineering For AI and SaaS Products (Hyderabad)
🏢 Quadrant IT Services
📍 Hyderabad