Advance your career in Assurance by joining our team focused on Sustainability & Emerging Assurance. This role offers a unique opportunity to design, develop, and deploy cutting-edge Agentic AI solutions, AI Assistants, and workflow automation platforms powered by Large Language Models (LLMs). You'll integrate AI engineering, software development, cloud expertise, and MLOps to create intelligent, production-ready systems that streamline business processes and enhance knowledge discovery.

If you are passionate about the forefront of AI and thrive in a full-cycle AI development environment, from initial experimentation to ongoing optimization, we encourage you to apply. This position is crucial for driving operational efficiency and innovation through advanced AI technologies.

Develop and scale Agentic AI solutions, AI Assistants, and workflow automation platforms utilizing Large Language Models (LLMs).

Build, deploy, and optimize Retrieval-Augmented Generation (RAG) applications and enterprise knowledge systems.

Create robust pipelines for knowledge ingestion, extraction, indexing, and retrieval from diverse data sources.

Implement sophisticated retrieval techniques including semantic search, vector search, and hybrid search.

Design and develop scalable AI services, APIs, and cloud-native applications.

Construct browser-based automation solutions using tools like Playwright and Puppeteer.

Deploy, monitor, and optimize containerized AI applications in cloud environments.

Establish frameworks for AI testing, benchmarking, and evaluation to ensure high quality and reliability.

Apply software engineering best practices, including CI/CD and version control.

Collaborate with cross-functional teams to translate business needs into AI-driven solutions.

Continuously explore and integrate emerging AI technologies to foster innovation.

Demonstrate strong programming skills in Python for AI development, automation, and backend services.

Possess hands-on experience developing AI, Machine Learning, and data-driven solutions.

Exhibit practical experience building applications powered by LLMs and Generative AI.

Show a solid understanding of Agentic AI architectures and workflow orchestration.

Have experience designing and implementing RAG solutions, embeddings, and vector databases.

Familiarity with data ingestion, web scraping, and unstructured data processing.

Proficiency with Git, software testing frameworks, and CI/CD pipelines.

Strong knowledge of cloud-native architectures, particularly Microsoft Azure services.

Experience with MLOps and LLMOps practices, including deployment and monitoring.

Ability to perform AI model evaluation, benchmarking, and optimization.

Hands-on experience with browser automation tools such as Playwright and Puppeteer.

Knowledge of API development, distributed systems, and scalable platform engineering is essential.