Generative AI Engineer

Job Description

The AI Engineer with GenAI expertise is responsible for developing advanced technical solutions, integrating cutting-edge generative AI technologies. This role requires a deep 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 proficiency in leading projects and ensuring delivery excellence. 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 (i.e., Kubernetes/CNCF) and partner technologies. - Collaborate on joint technical projects with partners, including Google, Microsoft, AWS, IBM, Red Hat, Intel, Cisco, and Dell/VMware. Mandatory Skills & Experience. : - A passionate developer with 7+ years of experience in Java, Python, and Kubernetes, comfortable working as part of a paired/balanced team. - Extensive experience in software development, with significant exposure to AI/ML technologies. - Expertise in GenAI frameworks : Proficient in using GenAI frameworks and libraries such as LangChain, OpenAI API, and Hugging Face Transformers. - Prompt engineering : Experience in designing and optimizing prompts for various AI models to achieve desired outputs and improve model performance. - Strong understanding of NLP techniques and tools, including tokenization, embeddings, transformers, and language models. - Proven experience developing complex solutions that leverage cloud-native technologies-featuring container-based, microservices-based approaches; based on applying 12-factor principles to application engineering. - Exemplary verbal and written communication skills (English). - Positive and solution-oriented mindset. - Solid experience delivering Agile and Scrum projects in a Jira-based project management environment. - Proven leadership skills and the ability to lead projects to ensure delivery excellence. Desired Skills & Experience : - Machine Learning Operations (MLOps) : Experience in deploying, monitoring, and maintaining AI models in production environments using MLOps practices. - Data engineering for AI : Skilled in data preprocessing, feature engineering, and creating pipelines to feed AI models with high-quality data. - AI model fine-tuning : Proficiency in fine-tuning pre-trained models on specific datasets to improve performance for specialized tasks. 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 SMLs, and integrating with such models. - Proficient with Kubernetes and other cloud-native technologies, including experience with commercial Kubernetes distributions (e., Red Hat OpenShift, VMware Tanzu, Google Anthos, Azure AKS, Amazon EKS, Google GKE). - Deep understanding of core practices including DevOps, SRE, Agile, Scrum, Domain-Driven Design, and familiarity with the CNCF open-source community. - Recognized with multiple cloud and technical certifications at a professional level, 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).