AI-ML Engineer

Your Role: 

As a Senior AI-ML Engineer for our AI-ML product portfolio, you will be responsible for the design, development, and production-grade deployment of AI-ML products and their features. You will work in close collaboration with product owner(s), data scientist(s), platform engineer(s), and architect(s) in feature realization, prototyping, engineering, and industrialization. Features can be integrations with public or in-house proprietary platforms/databases, products within our Data & AI ecosystem, AI-ML APIs (open-source in-house hosted or external SaaS APIs), and/or LLM agents that collectively are bound to offer scalable and reliable engines that can operate within a microservice architecture. You will operate within agile design and delivery cycles, informing and influencing our reference architecture through feedback based on your first-hand experience. The ideal candidate will be thorough with emerging technologies, products, and applications and can customize them to help our business become more secure and efficient. Your contribution will impact our customers and our organization and elevate AI-ML product strategy to the next level!   

Who you are:  
• You have a degree in Information Technology, Computer Science, Artificial Intelligence or similar fields
• Minimum 5 years of experience in design, development, and production-grade deployment of (Gen-)AI, ML, and/or NLP applications

• You can oversee technical stakeholders like developers/engineers within the product development team.
• Experience with tools and techniques used in the framework of traditional data science such as predictive modeling, forecasting, recommender engines, or similar areas is necessary.
• Experience with cloud-native AI-ML services offered by AWS (preferred) and/or Azure.

• Experience with LLM application development frameworks such as Langchain ,  LlamaIndex , Haystack, or Semantic Kernel is highly desired.
• Experience with good API design practices, containerization, microservices, and deployment with container orchestration services such as AWS ECS or Kubernetes is necessary.
• Experience with version control (git), automated testing, continuous integration, deployment, and monitoring
• Experience with open-source or proprietary vector databases such as FAISS, Qdrant, or similar technologies is a plus.
• Experience with popular libraries for Transformers models with the ability to fine-tune and performance-tune is a plus
• Experience with MLOps and AI-ML Observability frameworks, and hands-on experience with deployment and life cycle management of AI/ML/NLP models is a bonus
• You can work cross-functionally.