AI Engineering Manager

Role

AI Engineering Manager responsible for leading multiple engineering teams building AI-powered applications and enterprise software. The role combines deep technical expertise, AI engineering capabilities, product thinking, business understanding, and people leadership to drive responsible AI adoption and measurable engineering outcomes.

Compensation

• Salary: ₹30 LPA (listed pay range: ₹2,000,000 – ₹3,000,000 per year)

Key Responsibilities

1. AI-First Engineering Leadership

• Lead multiple engineering teams focused on AI-powered applications and enterprise software.
• Drive adoption of AI across development, testing, documentation, and delivery workflows.
• Establish engineering standards for AI-assisted development and quality assurance.
• Mentor engineers on AI-native development practices and modern engineering workflows.
• Build a high-performance engineering culture focused on ownership, innovation, quality, and continuous improvement.

2. Technical & Architecture Leadership

• Architect scalable, secure, cloud-native applications.
• Design enterprise-grade backend systems using Python and JavaScript/TypeScript.
• Lead API-first, event-driven, and distributed system architectures.
• Make and review technical and architectural decisions; conduct architecture and code reviews.

3. AI Engineering

• Design and build production-grade LLM-powered applications, agentic AI, and autonomous workflow systems.
• Implement Retrieval-Augmented Generation (RAG) solutions, multi-agent systems, and integrate models from OpenAI, Anthropic, Google, and open-source ecosystems.
• Establish frameworks for AI evaluation, monitoring, reliability, and optimization.

4. MCP & Intelligent Integrations

• Design and implement Model Context Protocol (MCP) servers and integrations.
• Build secure connections between AI agents and enterprise applications (CRMs, ERPs, databases, APIs, workflows).
• Design auth/authz, permissions, and governance models for MCP-based systems.

5. AI-Assisted Development & Engineering Productivity

• Champion responsible AI-assisted development and Vibe Coding practices.
• Leverage AI coding assistants to improve engineering velocity and developer productivity.
• Define standards for prompt engineering, AI-generated code validation, testing, code review, and documentation.
• Automate repetitive engineering processes and track measurable productivity outcomes.

6. Product & Business Thinking

• Translate business and customer problems into scalable technical solutions.
• Work with Product, Sales, Delivery, and customers to align technical solutions with business value.
• Contribute to product strategy, technical roadmaps, and solution architecture while balancing execution speed and technical quality.

7. Team Management & Mentorship

• Lead, mentor, and develop high-performing engineering teams.
• Coach engineers on architecture, system design, and AI engineering practices.
• Conduct technical interviews and contribute to hiring; set expectations for ownership and delivery.

Requirements

Experience & Qualifications

• Overall experience: 8–15 years in software engineering.
• Leadership experience: 3+ years leading engineering teams, conducting interviews, and managing talent.
• Hands-on experience building AI-powered enterprise products and deploying production-grade LLM applications.
• Practical experience with Model Context Protocol (MCP) servers and AI tool integrations.
• Strong background in system design, software architecture, and scalable SaaS/enterprise products.
• Preferred domain experience: SaaS, PropTech, HealthTech, FinTech, or Enterprise Technology.
• Proven track record of delivering scalable, production-ready products end-to-end.

Required Technical Skills

• Programming languages: Python; JavaScript/TypeScript.
• AI & ML: LLM integration, AI Agents/Agentic AI, multi-agent systems, prompt engineering, RAG, vector databases, embedding models, AI evaluation & observability, agent orchestration frameworks.
• MCP & Integrations: Model Context Protocol (MCP), MCP servers, AI tool integration/orchestration, secure enterprise integrations, auth/authz for AI systems.
• Backend & APIs: FastAPI, Django, Flask, Node.js, Express.js, REST APIs, GraphQL, WebSockets.
• Cloud & DevOps: AWS, Azure, GCP, Docker, Kubernetes, CI/CD pipelines, Infrastructure as Code, GitHub Actions, monitoring & observability.
• Databases: PostgreSQL, MongoDB, Redis, vector databases (Pinecone, Weaviate, Qdrant, Chroma, etc.).

Success Metrics (First 12 Months)

• Increased engineering productivity via responsible AI adoption.
• Improved code quality and fewer production defects.
• Successful delivery of enterprise-grade AI products.
• Established AI-first engineering standards and MCP-enabled solutions.
• Improved delivery predictability and engineering efficiency; increased AI maturity across teams.

Location

• Work location: In person

What We Offer

• Opportunity to build next-generation AI products and enterprise solutions for global customers.
• Hands-on work with LLMs, AI Agents, MCP, RAG, and autonomous workflows.
• High-impact leadership role shaping technology strategy, architecture, and team culture.
• Continuous opportunities for technical and leadership growth in an AI-first culture.