Senior Full Stack (React + Python ) Developer Role

You will work closely with architects, TPMs, product managers, QA engineers, DevOps engineers, designers, and peer developers to translate solution intent into scalable, maintainable, and production-ready systems.

Key responsibilities include:

• Develop robust, efficient, and maintainable code for critical features, ensuring both functional and non-functional requirements are met.
• Participate in system and solution design discussions, contributing practical implementation insights, technical trade-offs, and pseudo-code where needed.
• Act as a technical point of contact for day-to-day implementation issues, troubleshoot challenges quickly, and drive timely resolution.
• Enforce and improve code quality through code reviews, engineering standards, automated testing, and secure development practices.
• Collaborate closely with architects, TPMs, product teams, and platform teams to translate high-level designs and AI use cases into scalable, secure, and production-ready solutions.
• Build and integrate AI-powered application capabilities such as copilots, intelligent assistants, summarization, search, recommendations, and workflow augmentation using LLM APIs and related services where relevant.
• Implement AI-enabled solution patterns including prompt orchestration, retrieval-augmented generation (RAG), grounding strategies, structured outputs, tool or function calling, and quality or safety guardrails for enterprise use cases.
• Support and improve DevOps practices including build pipelines, CI/CD workflows, automated testing, release readiness, and engineering observability.
• Monitor, troubleshoot, and optimize application, system, and AI feature performance in line with project objectives, reliability expectations, latency targets, and cost considerations.
• Document technical designs, implementation decisions, and operational considerations to support maintainability, auditability, and knowledge transfer.
• Mentor junior engineers by sharing best practices, reviewing work products, and guiding them on sound engineering approaches.
• Contribute to technical proposals, estimations, reusable assets, and implementation improvements including automation, refactoring, and component reuse.
• Maintain compliance with security, quality, governance, and audit expectations, and contribute to broader process and capability improvement initiatives within CDT.

Must Have

• 6+ years of hands-on experience building enterprise-grade full-stack applications, with strong ownership of complex engineering workstreams from design through delivery.
• Strong expertise in one or more modern front-end frameworks such as React, , along with solid command of JavaScript/TypeScript, HTML5, and CSS3.
• Strong back-end engineering experience in at least one major stack such as Python (FastAPI, Django, Flask).
• Strong understanding of system design fundamentals, design patterns, API design, scalability, reliability, performance optimization, and distributed application architecture.
• Experience with relational and/or NoSQL databases such as MySQL, PostgreSQL, SQL Server, MongoDB, or similar, including schema design and query performance tuning.
• Hands-on experience building and consuming REST APIs and working with modern integration patterns across services and external systems.
• Strong understanding of responsive web design, reusable component-based architecture, accessibility basics, and front-end performance optimization.
• Hands-on experience with automated testing practices including unit, integration, and end-to-end testing using tools such as Jest, PyTest, Playwright, Cypress, or equivalent.
• Working experience with cloud platforms such as Azure, AWS, or GCP, including deployment, configuration, observability, and support for cloud-native environments.
• Hands-on experience with containerization and modern engineering environments using tools such as Docker, Kubernetes, or equivalent platforms.
• Strong understanding of DevOps and secure SDLC practices including CI/CD pipelines, source control workflows, code reviews, build automation, and release quality controls.
• Hands-on experience integrating AI capabilities into applications, including LLM APIs, prompt design, structured outputs, tool or function calling, and building reliable user-facing AI features.
• Good understanding of retrieval-augmented generation (RAG), vector search, grounding strategies, prompt orchestration, and common GenAI failure modes such as hallucination and response inconsistency.
• Experience implementing evaluation, monitoring, and guardrails for AI-enabled features, including response quality checks, fallback handling, latency and cost awareness, and privacy or security considerations for enterprise use cases.
• Ability to translate solution architecture into detailed implementation plans and contribute effectively to technical design discussions with architects and engineering leads.
• Strong troubleshooting and debugging skills, with the ability to rapidly resolve production and implementation issues while maintaining engineering quality.
• Experience mentoring junior engineers and contributing to engineering best practices, reusable assets, and continuous improvement initiatives.
• Excellent problem-solving, collaboration, and communication skills, with the ability to work effectively in cross-functional and client-facing delivery environments.

Nice to Have

• Experience working in Agile delivery environments with close collaboration across product, QA, DevOps, TPM, and architecture teams.
• Exposure to performance tuning, observability, logging, monitoring, caching, and reliability engineering practices in production systems.
• Experience contributing to reusable frameworks, shared components, internal accelerators, or broader engineering capability-building initiatives.
• Prior experience supporting estimations, technical proposals, solutioning discussions, or client-facing technical workshops.
• Exposure to AI engineering frameworks and tooling such as LangChain, LangGraph, Semantic Kernel, vector databases, model gateways, or similar ecosystems for building production-grade GenAI applications.
• Experience with LLMOps or MLOps practices such as prompt versioning, evaluation pipelines, experiment tracking, workflow observability, and responsible AI controls.
• Exposure to building AI copilots, agentic workflows, recommendation systems, or domain-specific GenAI solutions for enterprise products.

What success looks like in this role

• Own critical full-stack feature implementation and deliver high-quality solutions with minimal oversight.
• Convert architecture intent into scalable, secure, and production-ready implementations across core and AI-enabled application capabilities.
• Uphold strong engineering standards across code quality, testing, security, documentation, performance, and DevOps practices.
• Act as a dependable technical anchor by resolving implementation issues quickly and collaborating effectively across functions.
• Drive continuous improvement through reuse, automation, maintainability, mentoring, and contribution to team capability building.
• Deliver reliable AI-enabled experiences with appropriate quality, safety, observability, and cost awareness.

If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!

Hiring Related Queries

India: HiringsupportIndia@fractal.ai

Outside India: HiringsupportROW@fractal.ai

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