Full stack Developer with AI

Role: Senior Full-Stack Developer

Experience Level:

Key Skills & Requirements:

1. Full-Stack Web Development

Core Technologies:

• JavaScript/TypeScript

  - Advanced proficiency with ES6+, async/await patterns

• Node.js

  - Server-side development, API design, microservices architecture

• React/Vue.js

  - Modern component-based frontend development with hooks, state management

• HTML5/CSS3

  - Semantic markup, responsive design, CSS Grid/Flexbox

• Web Server Management

  - Nginx configuration, reverse proxy setup, SSL/TLS

Enterprise Requirements:

• Experience with

  enterprise-scale applications

• Microservices architecture

• GraphQL

  implementation and schema design

2. AWS Serverless Architecture(Must-Have)

Core AWS Services:

• AWS Lambda

  - Function design, cold start optimization, event-driven architecture

• API Gateway

  - REST/GraphQL APIs, request validation, throttling

• S3

  - Object storage, static hosting, data lakes, event triggers

• DynamoDB

  - NoSQL design patterns, GSI/LSI optimization

• CloudFormation/CDK

  - Infrastructure as Code

Advanced AWS:

• Amazon Bedrock

  - LLM integration, model selection, prompt optimization (Highly Preferred)

• EventBridge

  - Event-driven workflows, service decoupling

• Step Functions

  - Complex workflow orchestration

• CloudWatch

  - Monitoring, alerting, custom metrics

• VPC/Security Groups

  - Network security, service isolation

3. AI/ML & LLM Integration(Critical)

LLM Platforms:

• OpenAI API

  - GPT models, function calling, structured outputs

• Anthropic Claude

  - API integration, prompt engineering best practices

• Amazon Bedrock

  - Multi-model integration, guardrails implementation

AI/ML Concepts:

• Prompt Engineering

  - Chain-of-thought, few-shot learning, prompt optimization

• Context Management

  - Token optimization, conversation state, memory systems

• Vector Databases

  - Embedding generation, semantic search, RAG implementation

• Agent Frameworks

  - Multi-agent systems, tool integration, workflow orchestration

• RAG Systems

  - Retrieval-augmented generation, knowledge base integration

Bonus Skills:

• Fine-tuning

  - Model customization, training pipeline management

• MLOps

  - Model deployment, monitoring, A/B testing

4. Enterprise Integration & Data

Database Technologies:

• SQL

  - PostgreSQL, MySQL, complex queries, performance optimization

• NoSQL

  - MongoDB, DynamoDB, data modeling, scaling patterns

• Vector Databases

  - Pinecone, Weaviate, Milvus for AI applications

• Redis

  - Caching strategies, session management, pub/sub patterns

Integration Patterns:

• REST/GraphQL APIs

  - Design, versioning, documentation (OpenAPI/Swagger)

• Message Queues

  - SQS, SNS, RabbitMQ for asynchronous processing

• Event Streaming

  - Apache Kafka, real-time data processing

• ETL/ELT

  - Data pipeline design, transformation logic

Preferred Experience:

• Enterprise AI

  development and deployment

• Large-scale system

  architecture

• Multi-tenant SaaS

  applications with enterprise security

• Real-time AI applications

  with sub-second response requirements

• Regulatory compliance

  experience (SOX, GDPR, SOC2)

What You'll Build:

• AI-powered enterprise applications

  serving thousands of users

• Serverless microservices

  with event-driven architecture

• Intelligent automation

  systems integrating LLMs with business processes

• Developer platforms

  enabling AI adoption across the organization