About the role
Seeking a highly experienced Python + AI/GenAI Application Lead with Python development and AI/GenAI experience. The ideal candidate should have strong practical expertise in designing and delivering enterprise-grade GenAI applications, RAG solutions, Agentic AI workflows, Vector Search platforms, and scalable data processing systems. The role requires a technical leader who can drive architecture, mentor teams, and lead end-to-end solution delivery.
Responsibilities:
- Own the technical direction and architecture of custom software solutions, leading teams through design and delivery. Set development standards and ensure code quality, scalability, and performance aligned to business objectives.
- Lead the design and development of AI-powered enterprise applications using Python.
- Build and deploy GenAI applications with natural language interfaces.
- Design and optimize RAG, Text-to-SQL, Semantic Search, and Agentic AI solutions.
- Integrate and orchestrate LLMs from OpenAI, Gemini, Anthropic, and similar platforms.
- Design and implement scalable data ingestion and processing pipelines.
- Develop and manage embeddings, vector stores, and retrieval frameworks.
- Establish monitoring, observability, and performance dashboards using Grafana.
- Drive technical design decisions, code reviews, mentoring, and best practices.
- Collaborate with stakeholders to deliver scalable and secure AI solutions.
Requirements:
- 5+ years of hands-on Python development experience.
- 2+ years of AI/GenAI/RAG and LLM application development.
Must Have Skills:
- Python and Backend
- 5+ years of hands-on Python development experience
- Python, OOP, Design Patterns, Unit Testing
- FastAPI, Flask, Django REST Framework
- SQL, Data Modeling, Shell Scripting
- Pandas, NumPy, DataFrames
- Authentication and Authorization (JWT-OAuth-SSO)
- Performance Tuning and Code Optimization
- AI/GenAI (Mandatory)
- 2+ years of hands-on AI/GenAI application development experience
- LLM Integration (OpenAI, Gemini, Anthropic, or similar)
- GenAI Tooling and Natural Language Interface Development
- Agentic AI Workflows
- RAG (Retrieval Augmented Generation) Development and Optimization
- Text-to-SQL Solutions
- LangChain, LangGraph, Sentence Transformers
- Hugging Face Transformers
- Vector Search and Semantic Search
- Embedding Models (OpenAI, BERT, or similar)
- Vector Databases (FAISS, Qdrant, Weaviate, or similar)
- Create and manage embeddings and vector stores
- Design and implement efficient data ingestion and processing pipelines
- Grafana Monitoring and Observability
- DevOps and Delivery
- Git, CI-CD
- Enterprise Application Development
- Solution Architecture and Technical Leadership
Good to Have:
- Azure OpenAI, AWS Bedrock.
- LlamaIndex, MCP.
- Docker, Kubernetes.
- AWS: Azure Cloud.
- Microservices Architecture.
- Additional Requirements.