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Lead AI Engineer
Quantanite
Mumbai, IND
Full-time
Lead · 5–7 years experience
Onsite
Discovered 1 weeks ago
Role Overview
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.
Full Job Posting
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 architecture or technical leadership capacity.
- Hands on experience with LLMs (OpenAI, Claude, Gemini, Llama, etc.), GenAI frameworks (LangChain, LlamaIndex), and RAG-based architecture.
- Working knowledge of vector databases (Pinecone, FAISS, ChromaDB, Weaviate).
- Strong full stack development background: Python, JavaScript/TypeScript, and microservices (FastAPI, Flask, Node.js).
- Experience designing and deploying cloud-native solutions, Azure preferred, including deployment architecture and containerization.
- Practical experience with reinforcement learning / fine-tuning / RLHF-style optimization techniques for AI agents or models.
- Experience designing memory/context architectures for AI agents — short-term vs. long-term memory, retrieval strategies, and context window management.
- Familiarity with MCP (Model Context Protocol) or similar service-interoperability standards; comfortable designing API-first, composable service architectures.
- Solid grounding in data engineering and integration patterns — ETL, APIs, and data pipelines.
- Familiarity with Git, CI/CD pipelines, secure coding practices, and code review processes.
- Ability to produce clear architecture documentation — solution blueprints, technology architecture views, and design specifications.
- Preferred Experience
- Experience in BPO/outsourcing or high-volume, multi-client product delivery environments.
- Experience end-to-end engineering across multiple concurrent product feature builds or client implementations in an agile/hyper-agile delivery model.
- Familiarity with orchestration tools (Airflow, Prefect, LangGraph).
- Exposure to voice AI, OCR, vision-based AI, or multimodal models.
- Soft Skills
- Able to explain technical trade-offs to non-technical stakeholders in plain terms.
- Comfortable making architectural calls under ambiguity and time pressure.
- High ownership — treats architecture quality as a personal accountability, not a checklist.
- Collaborative with Tech Lead and Tech PM; balances architectural rigor against delivery speed.
- Naturally curious and current with the latest AI/ML and GenAI developments.
Qualifications
- Familiarity with MCP (Model Context Protocol) or similar service-interoperability standards; comfortable designing API-first, composable service architectures.
- Familiarity with Git, CI/CD pipelines, secure coding practices, and code review processes.
- Familiarity with orchestration tools (Airflow, Prefect, LangGraph).
About Quantanite
IT Services & Consulting2500 employeesFounded 2014
Quantanite is a private UK-based outsourcing provider delivering AI-enabled customer operations and data services to global businesses.
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