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, Angular, or Vue, 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.
Section 4 (Others)
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