Artificial Intelligence Developer

Role Summary

As an AI Engineer at EY, you will collaborate with key decision-makers to solve complex client challenges. You will design and implement intelligence layers of multi-agent systems, integrating secure and performant AI agents powered by LLMs and Retrieval-Augmented Generation (RAG) architectures.

Key Responsibilities

• Implement AI solutions using various technologies to address client-specific needs.
• Stay updated on the latest AI trends and technologies.
• Design, develop, and deploy machine learning and AI models (predictive analytics, NLP, computer vision, recommendation systems, LLM-based workflows).
• Build and maintain scalable model training, evaluation, and inference pipelines.
• Implement MLOps best practices (versioning, CI/CD, monitoring, retraining, automation).
• Integrate AI models into production systems using APIs, microservices, or model-serving platforms.
• Fine-tune and optimize LLMs using frameworks like LangChain, RAG, and vector search.
• Collaborate with cross-functional teams to translate business requirements into technical AI solutions.
• Ensure AI solutions meet standards for accuracy, reliability, explainability, and ethical AI practices.

Requirements/Qualifications

• Experience: 5+ years of hands-on experience in AI/ML engineering or applied machine learning.
• Programming: Strong Python skills and familiarity with libraries (TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost, LightGBM).
• Machine Learning Concepts: Deep understanding of feature engineering, model evaluation, hyperparameter tuning, cross-validation, and interpretability.
• AI Platforms & Frameworks: Experience with Hugging Face Transformers, LangChain, and vector databases (Pinecone, Weaviate, FAISS, ChromaDB).
• Cloud ML Platforms: Hands-on experience with Azure (ML Studio, Cognitive Services), AWS (SageMaker, Bedrock, Rekognition), or GCP (Vertex AI, BigQuery ML).
• DevOps/MLOps: Experience with automated testing, CI/CD (Azure DevOps/GitHub Actions), containerization (Docker), and cloud cost management.
• Additional Skills: Familiarity with React for front-end integration, Generative AI applications, deep learning architectures (CNNs, RNNs, Transformers), and model governance/ethical AI compliance.
• Tools: Experience with Docker, Kubernetes, GitHub Actions, Terraform, and experimentation tracking (MLflow, Weights & Biases, Neptune.ai).
• Education: Bachelor’s or master’s degree in AI, Machine Learning, Data Science, IT, Statistics, Engineering, Computer Science, or related fields.
• Certifications: Relevant certifications (Azure AI Engineer Associate, AWS Machine Learning Specialist, Google Professional Machine Learning Engineer, Databricks Machine Learning Professional, or OpenAI/Hugging Face/Nvidia-based certifications).

Salary/Benefits

• Growth and learning opportunities
• Global exposure
• Purpose-led work