Senior Full stack/ Technical Lead - Python

We are seeking an experienced Senior Full Stack Developer / Technical Lead with strong expertise in Python-based backend engineering, Angular and TypeScript frontend development, and the design and implementation of AI/LLM-powered enterprise applications.

The candidate will lead technical design and implementation, establish engineering standards, mentor developers, and drive end-to-end solution delivery. The role requires a balance of hands-on coding, architecture, technical decision-making, stakeholder engagement, and delivery leadership.

The ideal candidate can translate complex business requirements into scalable technical solutions, guide teams through the software development lifecycle, and ensure that AI-enabled applications meet enterprise requirements for security, reliability, performance, and maintainability.

Key Responsibilities

Technical Leadership and Architecture

• Own the technical architecture and design of full-stack applications using Python, Angular, and TypeScript.
• Define scalable backend services, API contracts, frontend architecture, integration patterns, and data models.
• Establish coding standards, design principles, reusable components, and engineering best practices.
• Evaluate frameworks, tools, and architectural approaches based on business requirements, scalability, security, and maintainability.
• Lead technical discussions, design reviews, code reviews, and engineering problem-solving sessions.
• Identify technical risks, dependencies, performance bottlenecks, and opportunities for modernization.

Backend and Frontend Engineering

• Lead the development of backend services, microservices, and REST APIs using Python and frameworks such as FastAPI, Flask, or Django.
• Guide the development of modular, scalable frontend applications using Angular, TypeScript, HTML5, and CSS.
• Oversee database design, data access patterns, API integrations, authentication, authorization, and error handling.
• Ensure code quality through automated testing, peer reviews, static analysis, and continuous integration.
• Remain hands-on with critical development tasks, complex integrations, prototypes, and production issue resolution.

AI and LLM Solution Engineering

• Design and implement enterprise AI/LLM solutions using commercial or open-source foundation models.
• Define architectures for RAG, embeddings, vector search, document processing, and context retrieval.
• Design LLM-powered workflows, tool/function calling, and agentic AI patterns where justified by the use case.
• Evaluate orchestration frameworks such as LangChain or LlamaIndex and select suitable approaches based on solution needs.
• Establish practices for prompt versioning, model evaluation, output validation, observability, and regression testing.
• Address AI-specific risks, including hallucinations, prompt injection, sensitive-data exposure, and inconsistent model outputs.
• Define appropriate human-in-the-loop controls, fallback mechanisms, and escalation paths for business-critical workflows.
• Assess model quality, latency, token consumption, and operational costs to optimize solution performance and total cost of ownership.

Team Leadership and Delivery

• Lead, mentor, and develop a team of full-stack developers and AI engineers.
• Break down requirements into technical tasks, estimate effort, assign work, and monitor delivery progress.
• Drive sprint planning, technical execution, dependency management, and resolution of delivery blockers.
• Collaborate with product owners, business stakeholders, architects, QA, DevOps, and security teams.
• Communicate technical decisions, delivery risks, progress, and mitigation plans to stakeholders.
• Ensure solutions meet agreed acceptance criteria, delivery timelines, quality standards, and operational readiness.
• Promote knowledge sharing, engineering productivity, reusable assets, and continuous improvement.

Cloud, DevOps and Production Readiness

• Guide containerization, CI/CD, automated testing, and deployment practices.
• Oversee cloud-native application deployment across AWS, Azure, or GCP.
• Ensure appropriate application monitoring, logging, distributed tracing, and incident troubleshooting.
• Establish security, resilience, scalability, backup, and disaster-recovery considerations as applicable.
• Drive production readiness, documentation, support handover, and ongoing performance improvements.