Senior Lead AI Developer / AI Engineering Lead

Position Overview We are looking for a Senior Lead AI Developer (2-12 years) with strong hands-on experience in AI/ML engineering, Generative AI, Agentic AI, AI-assisted development tools (Cursor.ai, GitHub Copilot, Claude Code, Windsurf, etc.), enterprise product architecture, and full-stack product development. The role involves leading the design, architecture, development, and delivery of next-generation AI-powered CRM platforms, DIY automation systems, intelligent workflow engines, AI agents, copilots, APIs, and cloud-native SaaS products. The candidate will lead a team of AI developers, architects, and product engineers while driving AI innovation, AI governance, scalability, cybersecurity, and product strategy. This is a strategic technical leadership role for our next-generation AI + Cloud + Cybersecurity product ecosystem. Key Responsibilities: 1. AI Strategy & Technical Leadership Lead AI product architecture and technology decisions across multiple product lines. Define AI development standards, coding frameworks, reusable AI components, and best practices. Mentor AI developers, full-stack engineers, and product teams. Drive AI adoption, innovation, and engineering excellence across the organization. Work closely with executive leadership, product management, cybersecurity, and cloud teams. 2. AI-Assisted Development & Generative AI Use Cursor.ai or similar AI coding tools to accelerate development. Build AI-powered features: Smart recommendations Data extraction & reconciliation CRM insights and automation Workflow bots and AI chat assistants Design and implement Enterprise AI solutions using Large Language Models (LLMs). Develop AI Agents, Multi-Agent Systems, AI Copilots, and Autonomous Workflows. Implement LLM-based modules using APIs like OpenAI, Anthropic, etc. Build Retrieval-Augmented Generation (RAG) pipelines and knowledge management systems. Implement Prompt Engineering, Agent Orchestration, Tool Calling, and Context Management frameworks. Evaluate and integrate emerging AI technologies and foundation models. 3. Full-Stack Engineering & Solution Architecture Develop front-end modules using React.js, Tailwind, modern UI frameworks. Build backend services using Node.js / Python. Implement REST or GraphQL APIs for multi-tenant SaaS products. Create reusable components for CRM, Finance, and Operations systems. Design scalable enterprise-grade architecture for AI-enabled SaaS products. Lead architecture reviews, performance optimization, and technical governance. Ensure high availability, scalability, resiliency, and maintainability of applications. 4. Database, AI Data Layer & Knowledge Systems Design and manage database schemas (PostgreSQL preferred). Write optimized queries, migrations, and integrations. Handle secure storage of structured and unstructured data. Design Vector Database architecture (Pinecone, Weaviate, Chroma, Milvus, pgVector, etc.). Implement semantic search, embeddings, document indexing, and enterprise knowledge repositories. Build AI-ready data pipelines and data governance frameworks. 5. Integrations & AI Ecosystem Integrate with third-party APIs (CRM, finance apps, cloud services). Build secure connectors and automation scripts. Integrate AI platforms, AI models, cloud AI services, enterprise applications, and automation frameworks. Build AI-powered integrations with ERP, CRM, HRMS, BFSI, cybersecurity, and cloud platforms. Develop API gateways, event-driven architectures, and AI orchestration layers. 6. Product Development & Innovation Work closely with senior engineers, product managers, and UI/UX teams. Convert workflows into functional modules. Maintain clean code quality and participate in code reviews. Own end-to-end delivery of AI products from concept to production. Drive innovation initiatives, proof-of-concepts (POCs), and AI experimentation programs. Collaborate with business stakeholders to identify AI opportunities and product enhancements. 7. Security, Governance & Compliance Follow secure coding guidelines aligned with DPDP, SOC, and cloud practices. Ensure tenant isolation, input validation, and secure API design. Work with cybersecurity teams on VAPT fixes. Implement AI Governance, Responsible AI, Model Risk Management, and AI Security controls. Ensure compliance with DPDP, GDPR, SOC 2, ISO 27001, RBI, SEBI, and BFSI security requirements where applicable. Establish LLM security controls against prompt injection, model poisoning, data leakage, and AI threats. Lead secure AI architecture reviews and cybersecurity assessments. 8. Cloud AI, MLOps & LLMOps Design and deploy AI workloads on Azure, AWS, and Google Cloud. Implement MLOps and LLMOps pipelines for model lifecycle management. Build automated CI/CD pipelines for AI models and SaaS applications. Manage AI observability, monitoring, performance metrics, and governance. Optimize AI infrastructure costs and resource utilization. Required Skills (Must Have) 815 years of software engineering experience with at least 35 years in AI/ML or Generative AI. Experience using Cursor.ai or AI-assisted coding environments. Strong JS/TS skills and experience with React.js. Hands-on backend development with Node.js and/or Python. Solid database experience: PostgreSQL (preferred) Schema design, indexing, performance tuning Experience building APIs (REST/GraphQL). Strong understanding of cloud platforms — Azure/AWS/GCP. AI Expertise Hands-on experience with Generative AI, LLMs, Agentic AI, and RAG architectures. Experience with OpenAI, Azure OpenAI, Anthropic Claude, Gemini, Mistral, Llama, or equivalent models. Experience with LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or equivalent frameworks. Knowledge of vector databases, embeddings, and semantic search. Understanding of AI security and responsible AI practices. Leadership Experience leading engineering teams, technical architects, and AI initiatives. Strong stakeholder management and decision-making capabilities. Ability to drive product architecture and technology roadmaps. Good-to-Have Skills (Bonus) Experience with: LLMs, embeddings, vector databases LangChain / RAG pipelines Multi-tenant SaaS architecture CRM or finance system development Basic DevOps (GitHub Actions, CI/CD, Docker). Kubernetes, Terraform, Infrastructure as Code (IaC). AI Agent frameworks and workflow orchestration platforms. BFSI, FinTech, ERP, GRC, Cybersecurity, and Compliance domain experience. Experience building AI-powered SaaS products from concept to scale. Soft Skills Fast learner, comfortable with AI-driven workflows. Strong problem-solving and debugging skills. Good communication and ability to work in cross-functional teams. Ownership mindset with attention to detail. Strong leadership and mentoring capabilities. Strategic thinking with business and product orientation. Ability to manage multiple stakeholders and priorities. Strong presentation and executive communication skills. Qualifications Bachelor’s degree in computer science, IT, Engineering, or equivalent experience. Master’s degree in computer science, Artificial Intelligence, Data Science, or related field (preferred). Relevant AI/Cloud certifications preferred. 3–5 years of hands-on software engineering experience. Minimum 2–3 years of experience leading AI, ML, or Generative AI initiatives. Experience delivering enterprise-scale SaaS and AI products.