About the role
Role OverviewAs an AI Technical Manager, you will lead engineering teams in designing, developing, and delivering AI-native products and enterprise software. You will drive AI adoption across the engineering lifecycle, improve developer productivity through AI, mentor teams on modern engineering practices, and ensure every solution creates measurable business value.This is not a traditional engineering manager role. We are looking for someone who can think like a product leader, architect like a senior engineer, and execute like a startup founder.
Key ResponsibilitiesAI-First Engineering Leadership
- Lead multiple engineering teams developing AI-powered applications.
- Drive organization-wide adoption of AI across software development and delivery.
- Define engineering standards for AI-assisted development and quality assurance.
- Mentor developers in AI-native engineering workflows and best practices.
- Build a high-performance engineering culture focused on innovation and ownership.
- Architect scalable cloud-native applications.
- Design enterprise-grade backend systems using Python and JavaScript/TypeScript.
- Lead API-first and event-driven architectures.
- Design secure, scalable, and maintainable software systems.
- Review architecture, code quality, and technical decisions.
- Build and integrate LLM-powered applications.
- Design Agentic AI systems and autonomous workflows.
- Implement Retrieval-Augmented Generation (RAG) solutions.
- Develop multi-agent systems for enterprise automation.
- Integrate AI models from OpenAI, Anthropic, Google, and open-source ecosystems.
- Design AI evaluation, monitoring, and optimization frameworks.
- Design and implement Model Context Protocol (MCP) servers.
- Build secure integrations between AI agents and enterprise systems.
- Enable AI applications to interact with internal tools, CRMs, ERPs, databases, and business workflows through MCP.
- Establish governance, authentication, and permission models for MCP-based architectures.
- Champion high-quality AI-assisted development ("Vibe Coding") while maintaining enterprise-grade engineering standards.
- Leverage AI coding assistants to accelerate development without compromising code quality.
- Define standards for prompt engineering, code review, testing, and AI-generated code validation.
- Continuously improve engineering productivity through automation and AI tooling.
- Translate business problems into scalable technical solutions.
- Collaborate with product managers, sales, delivery, and customers.
- Make technology decisions based on business impact and customer value.
- Balance innovation with delivery timelines and technical debt.
- Lead, mentor, and grow engineering teams.
- Conduct technical interviews and hiring.
- Coach engineers in architecture, AI, system design, and modern software development.
- Drive performance, accountability, and continuous learning.
- Python (Expert)
- JavaScript / TypeScript (Expert)
- LLM Integration
- AI Agents
- Multi-Agent Systems
- Prompt Engineering
- Retrieval-Augmented Generation (RAG)
- Vector Databases
- Embedding Models
- AI Evaluation Frameworks
- Agent Orchestration Frameworks
- Strong understanding of Model Context Protocol (MCP)
- Experience designing and deploying MCP Servers
- Tool integration architecture
- AI tool orchestration
- Context management
- Secure enterprise integrations
- FastAPI
- Django
- Flask
- Node.js
- Express.js
- REST APIs
- GraphQL
- WebSockets
- AWS, Azure, or Google Cloud
- Docker
- Kubernetes
- CI/CD Pipelines
- Infrastructure as Code
- GitHub Actions
- Monitoring and Observability
- PostgreSQL
- MongoDB
- Redis
- Vector Databases (Pinecone, Weaviate, Qdrant, Chroma, or similar)
- Think beyond writing code and understand business outcomes.
- Build products instead of just delivering projects.
- Understand customer problems before proposing technology.
- Balance engineering quality with speed of execution.
- Drive measurable improvements in delivery efficiency and customer value.
- Influence technical strategy and product direction.
- 8–15 years of software engineering experience.
- 3+ years leading engineering teams.
- Hands-on experience building AI-powered enterprise products.
- Experience deploying production-grade LLM applications.
- Experience with MCP servers and AI tool integrations.
- Experience in SaaS, PropTech, HealthTech, FinTech, or enterprise software.
- Strong system design and software architecture expertise.
- Proven track record of delivering scalable products.
- Product mindset over project mindset.
- Strong ownership and accountability.
- Excellent problem-solving and analytical thinking.
- Passion for AI and continuous learning.
- Strong communication and leadership skills.
- Ability to mentor and inspire engineering teams.
- High standards for engineering quality and customer experience.
- Increase engineering productivity through responsible AI adoption.
- Improve code quality and reduce production defects.
- Successfully deploy enterprise-grade AI products.
- Establish AI engineering best practices across teams.
- Mentor developers in AI-first software development.
- Build scalable MCP-enabled enterprise solutions.
- Deliver projects on time with high customer satisfaction.
- Contribute to business growth through technology innovation.
- Build next-generation AI products used by global customers.
- Work on cutting-edge AI technologies, LLMs, Agents, MCP, and enterprise automation.
- Collaborate with a highly skilled AI-focused engineering team.
- Opportunity to influence technology strategy and product direction.
- Continuous learning, innovation, and leadership growth.
- Shape the future of AI-driven software engineering.