AI Technical Lead

Key Responsibilities • 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 work shops, 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 over see 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. QualificationsExperience • 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/Graph QL). • 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 • Chat GPT/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: Big Query, 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 & Gen AI • 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 • Auto ML 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 • Strong Understanding of:- Prompt engineering and optimization • RAG (Retrieval-Augmented Generation)architectures • Vector search and embeddings (Vertex AI Vector Search) • AI model evaluation and monitoring • Responsible AI practices and model governance Data & Analytics • Strong experience with Big Query for data warehousing, analytics, and ML feature engineering. • Understanding of data pipelines, ETL/ELT processes, and data integration patterns. • Experience with both SQL and NoSQL databases. • Familiarity with data streaming(Pub/Sub, Dataflow). • Understanding of data governance, privacy, and security best practices. DevOps & Security • Experience with modern DevOps practices and tools. • Understanding of security best practices across the full stack. • Knowledge of secure AI practices, data privacy, and compliance requirements. • Experience with monitoring and observability tools (Cloud Monitoring, Cloud Logging). Soft Skills • Strong problem-solving and analytical skills with ability to tackle complex full-stack and AI challenges. • Excellent communication and stakeholder management abilities. • Ability to explain technical concepts to both technical and non-technical audiences. • Ability to work effectively infast-paced, multi-project environments. • Passion for learning and staying current with emerging technologies in full-stack development, cloud, and AI. • Strong collaboration skills and team-oriented mindset. Nice to Have • Experience in other popular cloud platform such as Azure and AWS #J-18808-Ljbffr