AI & System Integration: Design and implement robust integrations between AI/ML models (LLMs, proprietary APIs) and enterprise systems. Lead the integration of services like OpenAI and Azure AI into production-grade applications.
Full Stack Development: Build high-performance backend services using Python (FastAPI/Flask) and Node.js/TypeScript. Develop modern, responsive front-end applications using React.js and Next.js.
AI/GenAI Enablement: Implement RAG (Retrieval-Augmented Generation) patterns and integrate vector databases into the application flow. Optimize prompts and orchestration workflows (e.g., LangChain) for accuracy and latency.
Architecture & Design: Define integration patterns (REST, GraphQL, Event-driven) and microservices architectures. Ensure all AI-driven workflows are fault-tolerant and scalable.
Security & Compliance: Apply industry-standard security protocols, including OAuth and JWT for authentication, and ensure strict adherence to data privacy (PII handling) and API security best practices.
Technical Mentorship: Provide guidance to junior developers, participate in deep-dive design reviews, and contribute to sprint planning in a fast-paced Agile environment.
Professional Tenure: 6–8 years of experience in full-stack software development, with at least 2 years focused on integrating AI/ML services into enterprise environments.
Python Expertise: Deep proficiency in Python with a strong focus on high-performance frameworks like FastAPI, Flask, or Django for building AI middleware.
JavaScript Ecosystem: Expert-level knowledge of Node.js with TypeScript and hands-on experience with React.js or Next.js (including Server-Side Rendering and API routes).
AI Integration Mastery: Proven experience integrating LLM APIs (OpenAI, Azure AI, Anthropic) and working with AI orchestration frameworks like LangChain or LlamaIndex.
RAG & Vector Knowledge: Practical understanding of Retrieval-Augmented Generation (RAG), word embeddings, and working with Vector Databases (e.g., Pinecone, Weaviate, Milvus, or FAISS).
API & Middleware Design: Strong experience designing and consuming RESTful and GraphQL APIs. Understanding of event-driven architecture and message brokers (Kafka/RabbitMQ) is a significant plus.
Database Proficiency: Experience with both SQL (PostgreSQL, MySQL) and NoSQL (MongoDB, Redis) databases, specifically regarding how they support AI metadata storage.
Security Frameworks: Mastery of secure authentication and authorization protocols (OAuth2, OpenID Connect, JWT) and experience handling sensitive data/PII in compliance with GDPR.
Modern DevOps Tools: Proficiency with Docker for containerization and experience working within CI/CD pipelines (GitHub Actions, GitLab CI, or Jenkins).
Architectural Patterns: Solid understanding of microservices architecture, design patterns (e.g., Circuit Breaker, Saga), and system scalability principles.
Soft Skills & Collaboration: Excellent problem-solving skills and the ability to communicate complex integration logic to non-technical stakeholders.
Language Skills: Native or C1-level English is mandatory for collaboration with our global architecture and data science teams.
Agile Methodology: Strong experience working in Agile/Scrum environments using Jira for tracking and delivery.
