Full Stack Architect - Generative AI

Job Description :

Position Overview :

We are seeking an experienced Full Stack Architect with Generative AI expertise to lead the design and implementation of scalable, enterprise-grade applications leveraging Generative AI-based product development. The ideal candidate will have a strong foundation in both frontend and backend architecture, cloud infrastructure, and practical experience building Gen AI-powered products.

Key Responsibilities :

Architecture & Design :

- Design and architect end-to-end full stack solutions combining traditional software architecture with Gen AI capabilities

- Define technical strategies for integrating LLMs, RAG systems, and vector databases into production applications

- Create scalable microservices and multi-agent architectures that leverage AI frameworks

- Establish architectural patterns, best practices, and technology standards for the organization

- Conduct design reviews and provide technical guidance to engineering teams

Generative AI Integration :

- Lead the design and implementation of AI-powered features using LangChain, LangGraph, and CrewAI frameworks

- Design and build RAG (Retrieval-Augmented Generation) pipelines for knowledge-driven applications

- Architect vector database solutions and embeddings strategies for semantic search and retrieval

- Evaluate and integrate different LLM providers and open-source models

- Develop prompt engineering strategies and fine-tuning approaches for domain-specific use cases

- Design agentic workflows and orchestration systems for multi-agent AI applications

Full Stack Development :

- Establish frontend architecture using React, Angular, or Next.js for AI-augmented user experiences

- Design backend systems using Node.js, Java, or Python with frameworks like Express.js, Nest.js, Spring Boot, or FastAPI

- Define database schemas and optimization strategies for both NoSQL and RDBMS systems

- Ensure proper API design, security, and performance optimization across the stack

Cloud & DevOps :

- Design cloud-native architectures on AWS, Azure, or GCP

- Establish CI/CD pipelines, containerization strategies (Docker, Kubernetes), and infrastructure as code

- Optimize cloud costs, scalability, and disaster recovery mechanisms

- Implement security best practices including authentication, authorization, and data encryption

Technical Leadership :

- Mentor and guide development teams on architectural decisions and implementation patterns

- Conduct technical due diligence on emerging AI technologies and frameworks

- Document architectural decisions and create technical specifications

- Collaborate with product, data science, and operations teams

Required Qualifications :

Experience :

- Minimum 10 years of overall software development and architecture experience

- Minimum 3 years as a Solutions/Systems/Full Stack Architect or equivalent technical leadership role

- Demonstrated experience building and deploying production Gen AI applications

Frontend Technologies :

- Expert-level proficiency with one of : React, Angular, or Next.js

- Modern component-based architecture and state management (Redux, Context API, RxJS)

- Responsive design, accessibility (WCAG), and performance optimization

- Knowledge of frontend build tools (Webpack, Vite, Next.js build systems)

Backend Technologies :

- Expert-level proficiency with one of : Node.js, Java, or Python

- Production experience with one of : Express.js, Nest.js, Spring Boot, or FastAPI

- RESTful API design, GraphQL, and async processing patterns

- Session management, caching strategies, and performance tuning

Generative AI & LLM Technologies :

- Hands-on experience with LangChain or LangGraph for building AI applications

- Practical knowledge of CrewAI or similar multi-agent orchestration frameworks

- Deep understanding of RAG (Retrieval-Augmented Generation) architecture and implementation

- Experience with vector databases (Pinecone, Weaviate, Milvus, Qdrant, or similar)

- Knowledge of embeddings, semantic search, and chunking strategies

- Familiarity with LLM fine-tuning, prompt engineering, and model evaluation

- Experience with multiple LLM providers (OpenAI, Anthropic, Google, open-source models like Llama)

Databases :

- Expert knowledge of MongoDB (mandatory) schema design, indexing, aggregation pipelines, and scaling

- Strong knowledge of RDBMS (PostgreSQL, MySQL, Oracle, or SQL Server)

- Understanding of database indexing, query optimization, and scaling strategies

- Experience designing for high-availability and fault tolerance

Cloud Platforms :

- Production experience with at least one major cloud provider : AWS, Azure, or GCP

- Expertise in core cloud services : compute, storage, databases, and networking

- Infrastructure as Code (Terraform, CloudFormation, or ARM templates)

- Containerization and orchestration (Docker, Kubernetes, or managed container services)

Additional Technical Skills :

- Message queues and event streaming (Kafka, RabbitMQ, AWS SQS/SNS)

- Authentication & authorization (OAuth 2.0, JWT, SAML, identity management)

- API Gateway patterns, rate limiting, and traffic management

- Caching strategies (Redis, Memcached, CDN optimization)

- Monitoring, logging, and observability tools (ELK, Prometheus, Datadog, CloudWatch)

- Testing frameworks and automation (unit, integration, e2e testing)

- Version control and Git workflows

- Agile and DevOps methodologies

Soft Skills :

- Exceptional problem-solving and analytical abilities

- Strong communication and technical documentation skills

- Ability to translate business requirements into technical architecture

- Experience with cross-functional collaboration and stakeholder management

- Mentoring and technical leadership capability

- Intellectual curiosity and commitment to continuous learning

Preferred Qualifications :

- Product development experience building and scaling Gen AI-powered products from conception to market

- Experience with distributed systems design and microservices architecture patterns (CQRS, Event Sourcing)

- Knowledge of advanced Gen AI concepts : fine-tuning, prompt optimization, token optimization

- Familiarity with enterprise security frameworks and compliance (SOC 2, HIPAA, GDPR)

- Open-source contributions or community involvement in Gen AI projects

- Experience building or optimizing cost-effective Gen AI solutions

- Knowledge of graph databases and knowledge graphs

- Understanding of Gen AI economics and token optimization strategies

- Experience with edge computing or serverless architectures for AI workloads

Technical Stack Summary :

Category : Technologies

Frontend : React, Angular, or Next.js

Backend : Node.js, Java, or Python with Express.js, Nest.js, Spring Boot, or FastAPI

Databases : MongoDB (mandatory), PostgreSQL, MySQL, Oracle, or SQL Server

Cloud : AWS, Azure, or GCP (compute, storage, databases, networking)

AI/ML Frameworks : LangChain, LangGraph, CrewAI, Hugging Face, LlamaIndex, Langsmith

Vector Databases : Pinecone, Weaviate, Milvus, Qdrant, Chroma, Faiss

LLM Providers : OpenAI, Anthropic, Google Vertex AI, AWS Bedrock, Cohere, Mistral, open-source models

DevOps/Infrastructure : Docker, Kubernetes, Terraform, GitHub Actions, GitLab CI, Jenkins

Message Queues : Kafka, RabbitMQ, AWS SQS/SNS, Azure Service Bus

Monitoring : Prometheus, Grafana, ELK Stack, Datadog, CloudWatch

Other Tools : Redis, Git, Linux, API Gateway patterns, gRPC