Lead AI Engineer

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 se…