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.

Qualifications

Requirements

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

- 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 BigQuery 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 in fast-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