AI Engineer

📋 Description

• Build multi-agent workflows to optimize banking processes.
• Develop and maintain data pipelines for processing, training, deployment.
• Train, fine-tune, and optimize neural networks, including LLMs, for risk and queries.
• Collaborate with data scientists and stakeholders to turn requirements into AI apps.
• Ensure AI models adhere to banking regs, data privacy (GDPR, CCPA), and ethics.
• Conduct experiments, evaluate model performance, and iterate for accuracy and efficiency.
• Monitor deployed solutions in production, troubleshoot, and update as needed.
• Stay updated on AI trends, especially NLP/LLMs, and share knowledge.
• Participate in code reviews, agile ceremonies, and CI/CD processes.

🎯 Requirements

• 4–10 years in AI/ML engineering focused on software dev and deployment.
• Strong Python software engineering with TensorFlow, PyTorch, scikit-learn, Huggingface.
• NLP projects involving text classification, entity recognition, or similar.
• Hands-on training and fine-tuning of neural networks for performance and scalability.
• Experience with LLMs and agentic solutions via LangChain/LangGraph/ADK.
• Cloud platforms AWS, Azure, or Google Cloud for deployment and data management.
• Solid ML concepts: supervised/unsupervised learning, metrics, bias mitigation.
• Experience with Git and agile methodologies.
• Excellent problem-solving and fast-paced, collaborative work style.

🎁 Benefits

• Experience in financial services domains like fraud detection or credit scoring.
• Familiarity with big data tools (Spark, Hadoop) and SQL/NoSQL databases.
• Knowledge of graph databases and knowledge graphs.
• Understanding DevOps, containerization (Docker, Kubernetes), and MLOps.
• Publications, open-source contributions, or certifications in AI/ML.
• Strong communication skills for presenting technical concepts.