We are seeking a highly skilled Python Tech Lead to drive the architecture, design, and delivery of AIenabled, cloud-native applications. This role combines deep hands-on Python expertise with technical leadership and ownership of scalable systems integrating Generative AI, LLMs, and modern backend architectures.
As a Tech Lead, you will be responsible for end-to-end technical decision-making, mentoring engineers, ensuring production-grade AI integration, and aligning engineering practices with business goals.
Technical Leadership & Architecture Ownership
Own end-to-end architecture and technical design of AI-powered backend systems.
Define scalable system architecture for Python-based microservices and distributed systems.
Lead architecture reviews and provide technical sign-off on solution designs.
Drive technical roadmap planning for AI-enabled products or client engagements.
Identify and mitigate technical and performance risks across delivery lifecycle.
Serve as the technical escalation point for production issues and system reliability concerns.
AI & Intelligent System Design:
Design and implement LLM-powered solutions including RAG pipelines, vector search integrations, chatbots, and intelligent APIs.
Architect responsible AI integration strategies ensuring security, cost efficiency, and compliance.
Evaluate and integrate AI frameworks (e.g., LangChain, LlamaIndex) based on architectural fit.
Ensure robustness of AI workflows through monitoring, fallback handling, and performance tuning.
Backend & Cloud Engineering:
Design and develop scalable applications using Python frameworks such as FastAPI, Flask, or Django.
Architect RESTful and event-driven APIs for AI-integrated systems.
Implement asynchronous processing, caching, and background task orchestration.
Design containerized deployments using Docker and Kubernetes.
Leverage AWS, Azure, or GCP services for scalable, cloud-native architecture.
Define CI/CD pipelines and Infrastructure as Code (Terraform, Pulumi).
Engineering Governance & Best Practices:
Conduct code reviews and enforce engineering standards.
Establish guidelines for AI-assisted development tools ensuring responsible and effective usage.
Ensure strong focus on scalability, maintainability, performance, and security.
Promote automated testing, observability, and production readiness practices.
Optimize infrastructure cost for AI and compute-intensive workloads.
Team Leadership & Collaboration :
Mentor and guide engineers in backend and AI engineering best practices.
Support sprint planning, estimation, and technical backlog refinement.
Collaborate with product, DevOps, data science, and stakeholders to translate business needs into technical solutions.
Foster a culture of innovation balanced with engineering discipline.
Required Skills & Experience
7–10+ years of strong hands-on Python experience (3.10+).
Proven experience architecting scalable backend systems.
Strong experience building and deploying AI/LLM-integrated applications.
Hands-on expertise with FastAPI, Flask, or Django.
Experience designing and deploying RAG workflows and vector search systems.
Solid understanding of distributed systems and microservices architecture.
Experience with Docker, Kubernetes, and cloud platforms (AWS/Azure/GCP).
Strong database experience (PostgreSQL, MongoDB, Redis).
Experience implementing CI/CD and Infrastructure as Code.
Strong understanding of API security, scalability, and performance optimization.
Experience mentoring engineers and leading technical discussions.
Exposure to AI agent frameworks and workflow orchestration tools.
Good to Have
Experience with vector databases (Pinecone, Weaviate, FAISS, ChromaDB).
Experience in client-facing or services delivery environments.
Contributions to open-source AI or backend systems.
Technical Leadership, System Architecture Design, AI System Design, LLM Integration, RAG Pipelines, Vector Search, Chatbot Development, Intelligent APIs, Python Development, FastAPI, Flask, Django, Microservices Architecture, Distributed Systems, RESTful APIs, Event-Driven Architecture, Asynchronous Processing, Caching Strategies, Background Task Orchestration, Docker, Kubernetes, Cloud Computing, AWS, Azure, GCP, CI/CD Pipelines, Infrastructure As Code, Terraform, Pulumi, Database Management, PostgreSQL, MongoDB, Redis, API Security, Performance Optimization, Scalability Engineering, Observability, Automated Testing, Production Readiness, Cost Optimization, Engineering Governance, Code Review, Technical Roadmap Planning, Risk Management, AI Frameworks, LangChain, LlamaIndex, Vector Databases, Pinecone, Weaviate, FAISS, ChromaDB, AI Agent Frameworks, Workflow Orchestration, Team Leadership, Mentoring, Stakeholder Collaboration