Lead Full-Stack Developer
Role details
Job location
Tech stack
Job description
We are seeking a senior full-stack engineer with deep Python expertise and experience in delivering enterprise-grade AI applications. This role will lead key Fraud AI initiatives, provide technical leadership to offshore teams, and serve as an onshore point of contact during U.S. business hours. The ideal candidate will have strong backend capabilities, particularly around AI services, agentic workflows, LLM integrations, and cloud deployment, complemented by experience building React front-ends that consume AI-powered services., * Lead offshore engineering resources and drive the successful delivery of AI initiatives.
- Design, develop, and enhance backend services using Python frameworks such as Flask, FastAPI, or Django.
- Build and improve React-based user interfaces that support AI-enabled workflows.
- Architect and implement production-grade Generative AI solutions, including LLMs, RAG pipelines, and agentic systems.
- Establish and enforce guardrails, governance, monitoring, and compliance controls for all AI solutions.
- Develop and maintain CI/CD pipelines and cloud deployment processes on platforms like AWS or Azure.
- Collaborate with Product, Design, Platform, Risk, and Compliance teams to deliver integrated solutions.
- Provide mentorship to other engineers, drive architectural decisions, and perform technical leadership functions.
Requirements
Backend Development (5+ Years)
- Expert-level Python development experience with frameworks like Flask, FastAPI, or Django.
- Proficiency in API design and development (REST, OpenAPI, Swagger).
- Experience with authentication and security protocols such as OAuth, OIDC, JWT, and RBAC.
Frontend Development (3+ Years)
- Experience with React (preferred) or Angular, including component architecture, state management, and routing.
- Knowledge of accessibility standards and UI testing practices.
Generative AI / Agentic AI (1+ Year)
- Understanding of LLM concepts, RAG design, and agentic AI systems.
- Proficiency in LangChain and/or LangGraph.
- Experience with prompt engineering and implementing AI guardrails.
Data & Infrastructure (3-5 Years)
- Experience with vector and relational databases, and Redis caching.
- Proficiency with Docker, Kubernetes, and CI/CD pipelines.
- Knowledge of observability, monitoring, and performance optimization for AI workloads.
- Experience with public cloud deployments (AWS or Azure)., * Experience with LLM observability tools.
- Familiarity with graph databases.
- Experience with Kafka or other messaging platforms.