Lead AI Engineer

Title - Lead AI Engineer

Job Description

Location: Mumbai, India

Reporting to: Global Technology Leader

About Quantanite

Quantanite is a customer experience (CX) and digital outsourcing solutions company helping fast-growing businesses and global brands rethink their operations. Through intelligent automation, GenAI, and exceptional people, we deliver measurable transformation and seamless service delivery across every touchpoint. Our global teams are passionate about innovation, agility, and purpose-driven results.

About the Role

We are seeking an Lead AI Engineer to own the end-to-end Lead Engineering for one or more of our AI-driven product and client engagements. This is a hands-on, product/project-scoped Lead Engineer role — someone who codes, prototypes, and sets the technical bar for how we build AI applications. You translate business requirements into a coherent technical solution, make the core stack and design decisions, and ensure what gets built is scalable, secure, and cost-efficient. You will personally develop proof-of-concepts to test new technical approaches, establish the engineering principles and minimum technical standards the team builds against, and design the service architecture that lets our AI capabilities plug into products and client systems cleanly. You will work closely with the team on day-to-day delivery and code-level design and the Tech PM who owns delivery timelines and prioritization, acting as the technical backbone that keeps architecture decisions sound as development moves fast and continuously.

Key Responsibilities

• Bridge business requirements and technical implementation — work with business stakeholders, Product, and the Tech PM to translate requirements into concrete technical designs and solution blueprints.
• Own the overall architecture and design for the product/project, including deployment architecture — application, data, integration, and infrastructure layers end to end.
• Determine the technology stack, frameworks, and LLM selection — evaluate and choose languages, frameworks, LLM providers, vector stores, and orchestration tools based on capability, cost, and maintainability.
• Establish AI engineering principles for the team — model selection criteria, prompting and evaluation standards, testing methodology, and what “production-ready” means for an AI feature.
• Build hands-on prototypes and POCs to test technical concepts, de-risk unproven approaches, and give the team a working reference before committing to a full build.
• Set minimum technical standards across AI applications for: reinforcement learning / fine-tuning approaches, memory and context management (“memory stacking”) for agents, data privacy and security, and token consumption / inference cost efficiency.
• Establish MCP/API-style service architecture frameworks so AI capabilities are interoperable and can be integrated quickly across products and client systems, instead of being rebuilt per use case.
• Design for scalability, latency, and cost-efficiency — define non-functional requirements and architectural patterns (caching, async processing, model selection, infra sizing) to meet performance and budget targets.
• Drive major design decisions through hands-on prototyping and trade-off analysis (build vs. buy, pattern selection), not just review meetings.
• Lead architecture reviews and trade-off analysis (build vs. buy, pattern selection) and sign off before implementation begins.
• Define integration architecture with internal and external systems — APIs, data exchange mechanisms, and connections to enterprise and third-party platforms.
• Ensure code reviews, quality checks, coding standards, and secure coding practices are followed across the team, in partnership with the Tech Lead.
• Own technical debt management — track, prioritize, and plan remediation so shortcuts taken for speed don’t compound unmanaged.
• Produce and maintain architecture documentation — solution blueprints, technology architecture views, design specifications, and decision records.
• Mentor the developers on architectural best practices without taking over day-to-day implementation.
• Evaluate emerging GenAI/LLM capabilities and recommend adoption where it improves the product or delivery speed.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Software Engineering, or a related field.
• 5-7 years of software engineering experience, including 2+ years in an architect…