Job Description
Job Overview:
We are seeking a skilled GenAI App Developer (or Full Stack Developer , Python Backend Developer , API Developer , Prompt Engineer ) with expertise in API development , backend logic , machine learning , and NLP to contribute to large-scale GenAI applications .
You'll work on API integrations , system performance optimization, and developing multi-agent workflows, all within a dynamic, collaborative environment.
Required Qualifications:
Proven experience in API development (e.g., FastAPI , Flask , Django ).
Strong knowledge of Python , machine learning ( PyTorch ), and NLP (e.g., spaCy ).
Expertise in API authentication (OAuth, API keys ) and API documentation (Swagger).
Experience with task queues ( Celery ) and multi-agent workflows .
Hands-on experience with databases (MySQL, PostgreSQL , BigQuery , NoSQL ).
Familiarity with caching (Redis, Memcached) and cloud platforms ( AWS , Google Cloud , Azure ).
Key Responsibilities:
API Integration & Development:
Identify and define API integration points , ensuring clear documentation.
Design, implement, and test API endpoints (e.g., /generate, /status).
Auto-generate API documentation using FastAPI & Swagger .
Implement rate limiting ( Flask-Limiter ) and authentication ( OAuth , API keys ).
LLM & NLP Integration:
Develop prompting logic for Large Language Models (LLMs) to ensure accurate responses.
Integrate machine learning frameworks (e.g., PyTorch ) and NLP libraries (e.g., spaCy ).
Design and implement multi-agentic workflows using patterns like actor model , publish-subscribe , and client-server .
Multi-Agentic System Design:
Build and coordinate multi-agentic systems , ensuring efficient task delegation, communication, and failure handling across agents.
Develop distributed task management using tools like Celery and Kubernetes .
Testing & Debugging:
Write unit/integration tests with Pytest .
Set up logging and monitoring for system health and debugging.
Database & Caching:
Integrate with MySQL , PostgreSQL , NoSQL (e.g., BigQuery , MongoDB ), and vector databases (e.g., Pinecone ).
Implement caching strategies (e.g., Redis , Memcached ) to optimize performance.
Security & Compliance:
Ensure secure API access and data protection (OAuth, API keys , input validation).
Highly Desirable Qualifications:
Experience with vector databases (e.g., Pinecone , Weaviate, Cloud-based AI search ( Azure AI Search ).
Knowledge of CI/CD pipelines and containerization (e.g., Docker , Kubernetes ).
Familiarity with API design tools (e.g., Postman ) and rate limiting ( Flask-Limiter ).
Tools & Technologies:
API Frameworks : FastAPI , Flask , Django
Machine Learning & NLP : PyTorch , spaCy
Task Management : Celery
Databases : MySQL , PostgreSQL , BigQuery , MongoDB , Pinecone , Weaviate
Caching : Redis , Memcached
Cloud Platforms : AWS , Google Cloud , Azure
Version Control : Git
Security & Monitoring : OAuth , API keys , Python logging module