AI Engineer

Description & Requirements

Position Summary

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 (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, blogs, and speak at industry events.

. Maintain hands-on technical credibility, stay ahead of industry trends, and mentor others.

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.

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.g., 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)