Role Description The Senior AI Engineer is a technical leader with deep expertise in AI/ML, Generative AI, Large Language Models (LLMs), and modern cloud-native application development. This role is responsible for the design, development, maintenance, and support of enterprise-grade software systems and for delivering scalable architectural solutions across Ansell. He/She should demonstrate advanced programming expertise, particularly in Python, with deep proficiency in AI-centric libraries such as TensorFlow, PyTorch, and Hugging Face Transformers. He/She is responsible for: - Building innovative software products using various software architecture patterns with solid design principles. - Designing, developing, and deploying Custom AI agents capable of autonomous decision-making and task execution using LLMs and multi-modal models. - Conceptualizing and developing products specifically using Large Language Models, including data acquisition, pre-processing, model training/tuning, deployment, and monitoring. - Performing truth analysis to assess the accuracy and effectiveness of Large Language Model outputs, comparing them to known, accurate data. - Developing target state architectures and validating with the development team. - Collaborating with Product Owner, product development team, and infrastructure team to ensure support of software development and testing. - Implementing and manipulating complex algorithms essential for developing and optimizing generative AI models. - Overseeing and maintaining cloud infrastructure (e.g., AWS, Azure) specifically for Large Language Model workloads, ensuring cost-efficiency and scalability. - Implementing RAG architectures to enhance response relevance using external knowledge sources. - Integrating Large Language Models into chatbot workflows for summarization, classification, and intelligent routing, and Agentic AI Agents. - Designing prompt chaining and semantic search flows for document-based and FAQ-based virtual assistants. - Designing and implementing knowledge-based search using AI-driven techniques (e.g., FAQ ingestion, document indexing). - Driving performance optimization, CI/CD integration, and code quality standards. - Establishing robust monitoring and alerting systems to track Large Language Model performance, data drift, and other key metrics, proactively identifying and resolving issues. - Participating in proof of concepts to assist in technology direction and enabling business strategy. - Conducting and assisting in end-to-end technical design for software products. - Being responsible for impact analysis and design modifications to existing systems to support new solutions. Qualifications - Bachelor’s or Master’s degree in Computer Science, IT, or related field. - Certified in at least one Cloud or AI-related Certification. Requirements - 9+ years of experience in New Product Development, and at least 3 years of experience as an AI engineer using LLMs, AI-driven products & agents. - Proven ability to define and deliver complex technical products involving AI/ML, recommendation engines, or analytics. - Demonstrated track record of delivering high-quality software products at scale. - Strong data analytics skills and experience defining and owning translation of data and end-user insights to drive customer value. - Proficiency in LLMs, AWS, Python, Java Spring Boot, Node.js, Angular, Microservices, TypeScript, JavaScript. - Experience with REST/SOAP APIs, databases (MongoDB, PostgreSQL), Redis. - Familiar with containerization, DevOps, and cloud (AWS, Azure). - Experience in Gen AI Technologies: Agentic AI, LLMs (OpenAI, Azure), LangChain, Semantic Kernel. - Knowledge of authentication (OAuth2, JWT, SAML) and enterprise-grade security. - Excellent communication and interpersonal skills, with the ability to collaborate effectively across diverse teams and stakeholders. - Demonstrated ability to thrive in a fast-paced, dynamic environment, managing multiple priorities and deadlines effectively. Knowledge and Skills - Strong technical understanding of AI/ML, data architecture, and system integration principles. - Experience with principles and best practices in software development, configuration management, and processes, including leading Agile methodology and planning. - Thorough knowledge of various Services in AWS or Azure specific to AI, Gen AI, and LLMs. - Strong knowledge of Generative AI architectures and methods, including chunking, vectorization, context-based retrieval and search. - Expertise in cloud platforms (e.g., AWS, Azure) for ML workloads, MLOps, DevOps, or Data Engineering. - Proven experience in MLOps, LLMOps, or related roles, with hands-on experience deploying and managing machine learning and large language model pipelines. - Deep knowledge of Docker frameworks and orchestration concepts (Kubernetes experience is a plus). - Deep knowledge of source code control and configuration management concepts, and experience with Git and Git workflows. - Ability to operate in a fast-paced, evolving environment and appropriately prioritize tasks. - Knowledge and understanding of industry trends and new technologies and the ability to apply trends to architectural and technical implementation needs. - Ability to translate algorithmic capabilities into actionable business insights and customer value. - Exceptional communication, documentation, and stakeholder management skills. - Experience with Agile product management tools (e.g., Jira, Confluence). - Experience with RAG (retrieval-augmented generation) and GenAI guardrails. - Prompt engineering and LLM safety governance. Desirable Job Competencies - Proactive Ownership – Takes initiative to identify opportunities for platform and algorithm improvement. - Knowledge of AI ethics and understanding how to apply Trustworthy AI to ensure safe, responsible, and ethical use of AI technology. - Passion for learning and exploring new generative AI technologies and methods. - Analytical & Technical Acumen – Understands data models and AI methods while maintaining focus on usability, scalability, and measurable impact. - Strategic Communication – Articulates complex technical ideas clearly to both technical and commercial audiences. - Innovative Problem Solving – Champions experimentation and creative solutions to expand digital capabilities. - Collaborative Leadership – Works effectively across global, cross-functional teams, fostering trust and alignment. - Agility in a Global Context – Adapts to shifting priorities and diverse cultural and business environments with resilience and flexibility.