MS-FDL-Sr AI Engineer-Senior - E

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Role Overview: We are seeking a highly skilled and experienced

Senior Data Scientist with a minimum of 4 years of experience in Data Science and Machine Learning, with a strong focus on NLP, Generative AI, LLMs, MLOps, Optimization techniques, and Agentic AI solution Architecture. In this role, you will play a key role in the development and implementation of AI solutions, leveraging your technical expertise. The ideal candidate should have a deep understanding of AI technologies and experience in designing and implementing cutting-edge AI Agents, workflows and systems. Additionally, expertise in data engineering, DevOps, and MLOps practices will be valuable in this role.

Key Responsibilities:

• Design and implement state-of-the-art Agentic AI solutions tailored for the financial and accounting industry.
• Develop and implement AI models and systems, leveraging techniques such as Language Models (LLMs) and generative AI to solve industry-specific challenges.
• Collaborate with stakeholders to identify business opportunities and define AI project goals within the financial and accounting sectors.
• Stay updated with the latest advancements in generative AI techniques, such as LLMs, Agents and evaluate their potential applications in financial and accounting contexts.
• Utilize generative AI techniques, such as LLMs and Agentic Framework, to develop innovative solutions for financial and accounting use cases.
• Integrate with relevant APIs and libraries, such as Azure Open AI GPT models and Hugging Face Transformers, to leverage pre-trained models and enhance generative AI capabilities.
• Implement and optimize end-to-end pipelines for generative AI projects, ensuring seamless data processing and model deployment.
• Utilize vector databases, such as Redis, and NoSQL databases to efficiently handle large-scale generative AI datasets and outputs.
• Implement similarity search algorithms and techniques to enable efficient and accurate retrieval of relevant information from generative AI outputs.
• Collaborate with domain experts, stakeholders, and clients to understand specific business requirements and tailor generative AI solutions accordingly.
• Establish evaluation metrics and methodologies to assess the quality, coherence, and relevance of generative AI outputs for financial and accounting use cases.
• Ensure compliance with data privacy, security, and ethical considerations in AI applications.
• Leverage data engineering skills to curate, clean, and preprocess large-scale datasets for generative AI applications.

Qualifications & Skills:

• Education – Bachelor’s/Master’s in Computer Science, Engineering, or related field (Ph.D. preferred).
• Experience – 4+ years in Data Science/Machine Learning with proven end-to-end project delivery.
• Technical Expertise – Strong in ML, deep learning, generative AI, RAG, and agentic AI design patterns.
• Programming Skills – Proficient in Python (plus R), with hands-on experience in TensorFlow, PyTorch, and modern ML/data stacks.
• GenAI Frameworks – Experience with LangChain, LangGraph, Crew, and other agentic AI frameworks.
• Cloud & Infrastructure – Skilled in AWS/Azure/GCP, containerization (Docker, Kubernetes), automation (CI/CD), and data/ML pipelines.
• Data Engineering – Expertise in data curation, preprocessing, feature engineering, and handling large-scale datasets.
• AI Governance – Knowledge of responsible AI, including fairness, transparency, privacy, and security.
• Collaboration & Communication – Strong cross-functional leadership with ability to align technical work to business goals and communicate insights clearly.
• Innovation & Thought Leadership – Track record of driving innovation, staying updated with latest AI/LLM advancements, and advocating best practices.

Good to Have Skills:

• Apply trusted AI practices to ensure fairness, transparency, and accountability in AI models.
• Utilize optimization tools and techniques, including MIP (Mixed Integer Programming).
• Deep knowledge of classical AIML (regression, classification, time series, clustering).
• Drive DevOps and MLOps practices, covering CI/CD and monitoring of AI models.
• Implement CI/CD pipelines for streamlined model deployment and scaling processes.
• Utilize tools such as Docker, Kubernetes, and Git to build and manage AI pipelines.
• Apply infrastructure as code (IaC) principles, employing tools like Terraform or CloudFormation.
• Implement monitoring and logging tools to ensure AI model performance and reliability.
• Collaborate seamlessly with software engineering and operations teams for efficient AI model integration and deployment.
• Familiarity with DevOps and MLOps practices, including continuous integration, deployment, and monitoring of AI models.

EY | Building a better working world EY is building a better working world by creating new value for clients, people, society and the planet, while building trust in capital markets. Enabled by data, AI and advanced technology, EY teams help clients shape the future with confidence and develop answers for the most pressing issues of today and tomorrow. EY teams work across a full spectrum of services in assurance, consulting, tax, strategy and transactions. Fueled by sector insights, a globally connected, multi-disciplinary network and diverse ecosystem partners, EY teams can provide services in more than 150 countries and territories.