GenAI Lead

We are seeking a skilled GenAI App Lead 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