AI Technical Lead

The Technical Lead is responsible for providing end-to-end technical leadership across AI-powered system integration projects, from solution design through delivery and support. This role requires strong full-stack development capabilities combined with deep expertise in Google Cloud Platform and AI services. The Technical Lead bridges business requirements and technical implementation, ensuring scalable, secure, and intelligent solutions for clients. This role works closely with project managers, architects, developers, partners, and client stakeholders. Key Responsibilities Technical Leadership - Lead technical design and architecture across applications, cloud, data, and integrations. - Translate business and functional requirements into technical solutions and architecture artifacts. - Define and enforce coding standards, design principles, and best practices. - Define AI-assisted development standards: tool selection, prompt libraries, code-review checklists for AI-generated output, and guardrails for handling client-sensitive code in AI tool contexts - Review solution designs and code to ensure quality, performance, and security. - Provide guidance on modern architectures (cloud-native, microservices, event-driven). - Architect production-grade multi-agent and agentic systems using orchestration frameworks - Establish LLMOps and AI evaluation frameworks - Lead foundation model selection and fine-tuning strategy - with understanding of data requirements, compute costs, and governance implications Delivery & Implementation - Oversee end-to-end technical delivery across the SDLC (design, build, test, deploy, support). - Troubleshoot critical technical issues and provide resolution guidance. - Ensure solutions meet NFRs (availability, scalability, performance, security). - Support deployment, migration, and environment setup activities. Stakeholder Management - Act as the main technical point of contact for clients and internal teams. - Participate in requirement workshops, technical discussions, and design reviews. - Communicate technical concepts clearly to both technical and non-technical stakeholders. Team Leadership - Guide and mentor developers and engineers across full-stack development, AI/ML integration, and cloud engineering. - Support capability building and knowledge sharing within the team. - Allocate technical tasks and oversee quality of deliverables across the technology stack. Governance & Compliance - Ensure adherence to enterprise architecture, security, compliance, and responsible AI standards. - Contribute to technical documentation such as HLD, LLD, ADRs, runbooks, and AI model documentation. - Support audits, risk assessments, and governance reviews related to AI, data, and cloud solutions. Pre-Sales Support (SI Context) - Provide technical input for proposals, solutioning, and estimations for full-stack AI projects. - Support RFP/RFI responses and client presentations with technical expertise in Applications, Google Cloud and AI.

checklistMust have

• Qualifications Experience -
• Minimum 8 years of hands-on experience in full-stack software engineering, system integration, or solution delivery, with at least 3 years in a technical lead or principal engineer capacity. -
• Minimum 3 years of demonstrated experience leading technical teams in medium to large projects involving cloud and AI technologies. -
• Experience in SI or consulting environments is highly preferred.
• Full-Stack Development Capability (Mandatory) Backend Development
• Strong programming experience in Python, Java, Golang, or Node.js. - Expertise in API design and development (REST/GraphQL).
• Solid understanding of microservices architecture and middleware integration.
• Experience with message queues, event-driven architectures, and asynchronous processing.
• Knowledge of backend frameworks and design patterns. Frontend Development.
• Hands-on experience with modern frontend frameworks such as React, Angular, or Vue.
• Strong understanding of frontend architecture, component design, and state management.
• Experience with responsive design, cross-browser compatibility, and API integration.
• Familiarity with UI/UX best practices, performance optimization, and accessibility standards.
• Knowledge of modern frontend tooling (Webpack, Vite, etc.).
• AI-Assisted Development (Vibe Coding)
• Must have hands-on experience with AI-assisted coding tools and practices.
• Proficiency with tools such as:- GitHub Copilot or similar AI pair programming assistants.
• Cursor IDE or AI-enhanced development environments - ChatGPT/Claude for code generation and problem-solving - Google Cloud Code Assist (Gemini, Antigravity etc)
• Experience with:- Rapid prototyping using AI-generated code - Prompt engineering for code generation.
• Code review and refinement of AI-generated solutions.
• Accelerated development workflows using AI assistance.
• Understanding of best practices for:- Validating and testing AI-generated code - Maintaining code quality while using AI tools - Balancing speed with security and maintainability - Effective prompt crafting for development tasks
• Google Cloud Platform - Strong understanding and experience using core GCP services:- Compute: Compute Engine, Cloud Run, Cloud Functions, GKE (Google Kubernetes Engine) - Storage: Cloud Storage, Filestore - Databases: Cloud SQL, Firestore, Bigtable - Data & Analytics: BigQuery, Dataflow, Pub/Sub - Networking: VPC, Cloud Load Balancing, Cloud CDN - Security & IAM: Identity and Access Management, Secret Manager, Cloud Armor -
• Experience with CI/CD pipelines on GCP (Cloud Build, Artifact Registry). - Hands-on experience with containers (Docker) and orchestration (Kubernetes/GKE). - Infrastructure as Code experience (Terraform, Cloud Deployment Manager) is a plus.
• Google Cloud AI & GenAI - Must have proven experience working with Google Cloud AI and Generative AI services.
• Hands-on experience with Vertex AI including:- Model training, deployment, and monitoring - AutoML capabilities - Custom model development and fine-tuning - Model versioning and lifecycle management -
• Experience integrating Google Cloud AI services:- Generative AI: LLMs (PaLM API, Gemini), prompt engineering, RAG patterns - Vision AI: OCR, image classification, object detection - Speech AI: Speech-to-Text, Text-to-Speech - Natural Language AI: sentiment analysis, entity extraction, translation - Recommendations AI