Lead AI Engineer2 Technical Skills • Engineering Stack: Strong hands-on proficiency in Python and/or .NET (C#, ASP.NET Core), Java, or Node.js. • AI & GenAI: Practical experience with GenAI APIs (OpenAI, Azure OpenAI), LangChain, LlamaIndex, RAG patterns, or agentic workflow frameworks. • Cloud & DevOps: Hands-on experience with AWS, Azure, or GCP—microservices, Docker, Kubernetes, and CI/CD pipelines (GitHub Actions, Azure DevOps). • Automation: Exposure to workflow orchestration engines, RPA tools, or custom automation scripts. • Enterprise Platforms: Familiarity with enterprise systems (Oracle, SAP, Salesforce, or ServiceNow) is a plus. • Certifications: Cloud Certification (AWS, Azure, or GCP) at associate/developer level is a plus. Soft Skills • Clear technical communication—able to explain code implementation to teammates and project managers. • Collaborative team player accustomed to working across global/offshore time zones. • Curious and self-motivated — proactively explores new AI tools, frameworks, and engineering patterns. Experience • Experience: 5–8 years of hands-on software engineering experience, with 3+ years as, Senior AI engineer • 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. Key Responsibilities 1. Hands-on Engineering & Delivery • Write production-grade code to build component-level features and API integrations based on technical designs and blueprints. • Actively contribute to daily engineering deliverables—developing backend microservices, driving unit testing, refactoring code, and resolving technical debt. • Conduct peer code reviews to ensure code quality, performance, maintainability, and adherence to team engineering standards. • Troubleshoot and resolve complex bugs, API integration issues, and performance bottlenecks within application microservices. AI & Engineering Framework Adoption • Learn, implement, and integrate for AI-native feature— including GenAI integration patterns, agentic workflows, and intelligent automation • 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. 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 Execution • Develop and deploy cloud-native services on AWS, Azure, or GCP using containers (Docker, Kubernetes), serverless functions, and event-driven patterns. • Implement intelligent automation components using workflow orchestration engines, RPA integrations, or custom AI-driven pipelines. Engineering Community & Mentoring • Mentor junior engineers—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. • Education • Bachelor's degree in Computer Science, Computer Engineering, Information Technology, or a related technical field is required.