Application Lead (Full - Stack) - Python + AI / GenAI

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.