Here is the updated job description with the company name removed, the experience requirements clearly highlighted in the qualifications, and the compensation specified at 30 LPA:

Job Description: AI Engineering Manager

Role Title: AI Engineering Manager Experience Required: 8–15 years overall (with 3+ years in engineering leadership) Compensation: ₹30 LPA

Role Overview

We are looking for an AI Engineering Manager who can lead engineering teams in the era of AI-assisted software development.

This is more than a traditional engineering management role. The ideal candidate will combine deep technical expertise, AI engineering capabilities, product thinking, business understanding, and strong people leadership.

You will lead teams building AI-native products and enterprise software, drive responsible AI adoption across the engineering lifecycle, improve developer productivity through AI, and ensure technology decisions translate into measurable business value. The right candidate should be able to think like a product leader, architect like a senior engineer, and execute with an ownership mindset.

Experience & Qualifications

- Overall Experience: 8–15 years of experience in software engineering.
- Leadership Experience: 3+ years of experience in leading engineering teams, conducting interviews, and managing talent.
- AI Expertise: Hands-on experience building AI-powered enterprise products and deploying production-grade LLM applications.
- Integrations: Practical experience with Model Context Protocol (MCP) servers and AI tool integrations.
- System Design: Robust background in system design, software architecture, and building scalable SaaS or enterprise software products.
- Domain Knowledge: Preferred experience in domains such as SaaS, PropTech, HealthTech, FinTech, or Enterprise Technology.
- Track Record: Proven history of delivering scalable, production-ready products end-to-end.

Key Responsibilities1. AI-First Engineering Leadership

- Lead multiple engineering teams building AI-powered applications and enterprise software.
- Drive the adoption of AI across software 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,



and 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 critical technical and architectural decisions while establishing standards for code quality, system scalability, security, maintainability, and performance.
- Conduct architecture and code reviews to ensure adherence to engineering standards.

3. AI Engineering

- Design and build production-grade LLM-powered applications.
- Architect Agentic AI and autonomous workflow systems.
- Design and implement Retrieval-Augmented Generation (RAG) solutions and multi-agent systems for enterprise automation.
- Integrate AI models from OpenAI, Anthropic, Google, and open-source ecosystems.
- Establish frameworks for AI evaluation, monitoring, reliability, and optimization.
- Ensure AI solutions are scalable, secure, measurable, and production-ready.

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, and business workflows).
- Design authentication, authorization, permissions, and governance models for MCP-based systems.
- Establish scalable architecture patterns for AI tool integration and orchestration.

5. AI-Assisted Development & Engineering Productivity

- Champion responsible, high-quality 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.
- Identify opportunities to automate repetitive engineering processes using AI while tracking measurable engineering productivity and delivery outcomes.

6. Product & Business Thinking

- Translate business and customer problems into scalable technical solutions.
- Work closely with Product, Sales, Delivery, and customers to understand business requirements.
- Make technology decisions based on customer value,



business impact, scalability, and delivery feasibility.
- Balance speed of execution with engineering quality and technical debt.
- Contribute to product strategy, technical roadmap, and solution architecture.

7. Team Management & Mentorship

- Lead, mentor, and develop high-performing engineering teams.
- Coach engineers on architecture, system design, AI engineering, and modern development practices.
- Conduct technical interviews and contribute to engineering hiring.
- Set clear expectations around ownership, quality, accountability, and delivery.
- Identify skill gaps and create opportunities for continuous technical development.

Required Technical Skills

- Programming Languages: Python (Expert), JavaScript / TypeScript (Expert)
- AI & Machine Learning: LLM Integration, AI Agents & Agentic AI, Multi-Agent Systems, Prompt Engineering, RAG, Vector Databases, Embedding Models, AI Evaluation & Observability, Agent Orchestration Frameworks
- MCP & AI Integrations: Model Context Protocol (MCP), MCP Servers, AI Tool Integration/Orchestration, Context Management, 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 through responsible AI adoption.
- Improved code quality and reduction in production defects.
- Successful delivery of enterprise-grade AI products.
- Establishment of AI-first engineering standards and practices.
- Development of scalable MCP-enabled enterprise solutions.
- Improved delivery predictability and engineering efficiency.
- Stronger technical capability and AI maturity across engineering teams.

What We Offer

- Salary: ₹30 LPA
- Opportunity to build next-generation AI products and enterprise solutions for global customers.
- Hands-on work with cutting-edge tech stacks: 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.

Pay: ₹2,000,000.00 - ₹3,000,000.00 per year

Work Location: In person

📌 AI Engineering Manager (India)
🏢 R.V SOLUTION
📍 India