Technical Architecture & Delivery

• Own technical architecture decisions on delivery engagements — designing component-level solutions that align with broader architecture blueprints defined by the Solution or Principal Architect.

• Actively participate in engineering delivery, bridging the gap between architectural intent and implementation — reviewing code, driving technical design sessions, and unblocking development teams.

• Ensure technical quality across deliverables — conducting architecture and code reviews, validating implementation against design standards, and driving resolution of technical debt.

• Manage technical risk on delivery projects and escalate architectural concerns to the Solution Architect.

AI & Engineering Framework Adoption

• Learn, implement, and advocate for AI-native engineering practices — including GenAI integration patterns, agentic workflows, and intelligent automation — within delivery teams.

• Contribute to the development and maintenance of AI engineering frameworks, solution accelerators, and reusable technical assets.

• Evaluate and experiment with new tools, APIs, and platforms in the AI and cloud-native space — bringing relevant innovations into delivery practice.

Technical Consulting & Problem Solving

• Engage with client technical teams to understand system landscapes, integration requirements, and technical constraints.

• Propose pragmatic, technology-forward solutions to complex engineering challenges across application modernization, AI integration, and cloud migration.

• Support the Solution and Principal Architect in pre-sales activities including technical assessments, proof-of-concept design, and effort estimation.

Cloud & Automation Implementation

• Design and implement cloud-native architectures on AWS, Azure, or GCP — including containerization (Kubernetes/Docker), serverless functions, and event-driven patterns.

• Implement intelligent automation components using workflow engines, RPA tools, or custom AI-driven pipelines.

Engineering Community & Mentoring

• Mentor senior engineers and developers — supporting technical upskilling on AI tools, cloud platforms, and modern engineering practices.

• Contribute to Jade's internal community of practice through knowledge sharing, documentation, and participation in architecture discussions.

QUALIFICATIONS

Education

• Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related technical field is required.

Experience

• 5–9 years of total experience in software engineering, with at least 2–3 years in a technical architecture, lead engineer, or tech lead role.

• Hands-on experience building and delivering enterprise-grade solutions across cloud, AI/ML, and application engineering.

• Experience working in client-facing delivery environments within IT services or consulting organizations is preferred.

Technical Skills

• Engineering Stack: Strong hands-on proficiency in .NET (C#, ASP.NET Core) and/or OpenSource stacks (Python, Java, Node.js). Experience across both stacks is a strong advantage.

• AI & GenAI: Practical exposure to Generative AI integration, LLM APIs (OpenAI, Azure OpenAI), LangChain, RAG patterns, or agentic workflow frameworks.

• Cloud: Hands-on experience with AWS, Azure, or GCP — microservices, containers (Docker/Kubernetes), serverless, and CI/CD pipelines (GitHub Actions, Azure DevOps).

• Intelligent Automation: Exposure to RPA tools, workflow orchestration engines, or AI-driven automation platforms.

• Enterprise Platforms: Familiarity with Oracle, SAP, Salesforce, or ServiceNow is a plus.

• Cloud Certification (any level — AWS, Azure, or GCP) is a plus.

Soft Skills

• Strong technical communication — able to explain architecture decisions clearly to engineering teams and non-technical stakeholders.

• Delivery-focused and hands-on — willing to roll up sleeves and contribute to implementation alongside leading architectural decisions.

• Curious and self-motivated — proactively explores new AI tools, frameworks, and engineering patterns.

• Collaborative team player — works effectively in onshore-offshore delivery models across time zones.