Artificial Intelligence Engineer

EY; Bengaluru; Sur place; INR 2 500 000 - 4 000 000; Plein temps

Résumé du poste

EY seeks a Senior Data Scientist to lead NLP and generative AI initiatives, including agentic AI design, LLMs, and MLOps. You will architect AI workflows for financial/accounting use cases, collaborate with stakeholders, and implement end‑to‑end pipelines with Redis vector stores and cloud services.

The ideal candidate has 4+ years in data science, deep learning expertise, and strong Python (plus R) skills, with hands‑on experience in TensorFlow/PyTorch and modern ML stacks.

Qualifications

• 4+ years in Data Science/Machine Learning with end‑to‑end project delivery.
• Strong in ML, deep learning, generative AI, RAG, and agentic AI design patterns.
• Proficient in Python (plus R) with TensorFlow, PyTorch, and modern ML/data stacks.
• Experience with LangChain, LangGraph, Crew, and other agentic AI frameworks.
• Experience with AWS/Azure/GCP, containerization (Docker, Kubernetes), CI/CD, and ML pipelines.
• Knowledge of responsible AI, fairness, transparency, privacy and security.

Responsabilités

• Design and implement state‑of‑the‑art Agentic AI solutions for financial/accounting.
• Develop AI models using LLMs and generative AI to solve industry challenges.
• Collaborate with stakeholders to define AI project goals in financial sectors.
• Stay updated on GenAI techniques and evaluate AI agents for applicability.
• Leverage APIs and libraries like Azure OpenAI and Hugging Face transformers.
• Build end‑to‑end pipelines and optimize data processing and model deployment.
• Use vector stores (Redis) and NoSQL databases for large AI datasets.
• Work with domain experts to tailor AI solutions and measure relevance.
• Ensure data privacy, security, and ethical considerations in AI apps.
• Curate, clean, and preprocess large datasets for generative AI use cases.

Connaissances

NLP

Generative AI

LLMs

MLOps

Agentic AI

Python

R

LangChain

Cloud (AWS/Azure/GCP)

Data Engineering

AI Governance

Collaboration

Innovation

Formation

Bachelor's/Master's in CS/Engineering

Ph.D. preferred

Outils

TensorFlow

PyTorch

Docker

Kubernetes

CI/CD

Terraform

Description du poste

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.