Job Summary
The AI Engineer with GenAI expertise is responsible for developing advanced technical solutions, integrating cutting-edge generative AI technologies. This role requires a foundational understanding of modern technical and cloud-native practices, AI, DevOps, and machine learning technologies, particularly in generative models. You will support a wide range of customers through the “Ideation to MVP” journey, demonstrating enthusiasm for learning and contributing to project success.
Key Responsibilities
Key Responsibilities Technical & Engineering Leadership • Develop solutions leveraging GenAI technologies, integrating advanced AI capabilities into cloud-native architectures to enhance system functionality and scalability. • Lead the design and implementation of GenAI-driven applications, ensuring seamless integration with microservices and container-based environments. • Create solutions that fully leverage the capabilities of modern microservice and container-based environments running in public, private, and hybrid clouds. • Contribute to HCL thought leadership across the Cloud Native domain with an expert understanding of open-source technologies (e.g., Kubernetes/CNCF) and partner technologies. • Collaborate on joint technical projects with partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware. Service Delivery • Engineer innovative GenAI solutions from ideation to MVP, ensuring high performance and reliability within cloud-native frameworks. • Optimize AI models for deployment in cloud environments, balancing efficiency and effectiveness to meet client requirements and industry standards. • Assess existing complex solutions and recommend appropriate technical treatments to transform applications with cloud-native/12-factor characteristics. • Refactor existing solutions to implement a microservices-based architecture.
Innovation & Initiative • Drive the adoption of cutting-edge GenAI technologies within cloud-native projects, spearheading initiatives that push the boundaries of AI integration in cloud services. • Engage in technical innovation and support HCL’s position as an industry leader. • Author whitepapers and blogs, and possibly speak at industry events. • Maintain hands-on technical credibility, stay ahead of industry trends, and contribute to team learning. Client Relationships • Provide expert guidance to clients on incorporating GenAI and machine learning into their cloud-native systems, ensuring best practices and strategic alignment with business goals. • Conduct workshops and briefings to educate clients on the benefits and applications of GenAI, establishing strong, trust-based relationships. • Perform a trusted advisor role, contributing to technical projects (PoCs and MVPs) with a strong focus on technical excellence and on-time delivery.
Skill Requirements
Mandatory Skills & Experience • A passionate developer with 3+ years of experience in Java, Python, and Kubernetes, comfortable working as part of a paired/balanced team. • Foundational experience in software development, with exposure to AI/ML technologies. • Proficiency in GenAI frameworks: Experienced in using GenAI frameworks and libraries such as OpenAI API. • Prompt engineering: Experience in designing and optimizing prompts for various AI models to achieve desired outputs and improve model performance. • Experience developing solutions that leverage cloud-native technologies—featuring container based, microservices-based approaches; based on applying 12-factor principles to application engineering. • Strong verbal and written communication skills (English). • Positive and solution-oriented mindset. • Experience delivering Agile and Scrum projects in a Jira-based project management environment.
Other Requirements
Desired Skills & Experience • Understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models. • AI ethics and bias mitigation: Knowledgeable about ethical considerations in AI and experienced in implementing strategies to mitigate bias in AI models. • Knowledgeable about vector databases, LLMs, and integrating with such models. • Proficient with Kubernetes and other cloud-native technologies, including experience with commercial Kubernetes distributions (e.g., Red Hat OpenShift, VMware Tanzu, Google Anthos, Azure AKS, Amazon EKS, Google GKE). • Understanding of core practices including DevOps, SRE, Agile, Scrum, Domain-Driven Design, and familiarity with the CNCF open-source community. • Recognized with cloud and technical certifications, ideally including AI/ML specializations from providers like Google, Microsoft, AWS, Linux Foundation, IBM, or Red Hat. Verifiable Certification • At least one recognized cloud professional / developer certification (AWS/Google/Microsoft)