Notice period: 15 days to immediate joiners
Hiring mode: Permanent
Job Summary
We are seeking a highly skilled Python Tech Lead to drive the architecture, design, and delivery of AI-enabled, 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.
Key Responsibilities
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.
