Senior Al Engineer

Description & Requirements Position Summary The Senior 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, showcasing leadership and decision‑making abilities while tackling complex challenges. 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 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 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 10+ years of experience in Java, Python, Node.js, and Spring programming, 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, Gemini, 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 inspire and manage teams. 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, XP, 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) #J-18808-Ljbffr