Lead AI Engineer

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 GCPmicroservices, 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 communicationable 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.