Senior Machine Learning Engineer I, AI Governance

Optro

37d•Remote

About The Position

We are seeking a passionate and skilled Machine Learning Engineer to join our effort to push the frontiers of risk management. This role offers the opportunity to work with cutting-edge Large Language Models (LLMs), utilizing techniques like Retrieval-Augmented Generation (RAG), Few-Shot Learning, Prompt Engineering, Fine-Tuning, Semantic Search, and Knowledge Distillation, etc. You will apply your expertise to build advanced AI/ML solutions, enhancing our AuditBoard product portfolio with features like chat systems, agentic workflows, intelligent data extraction, and personalized insights. If you're excited about leveraging modern AI/ML techniques to transform the industry, this role is for you. Join our customer-focused team dedicated to continuous learning and innovation.

Requirements

Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, or a related field
4 plus years of hands-on experience in developing and deploying machine learning models
Ability to write scalable production-quality code
Proficiency in classical machine learning methods and familiarity with newer techniques like LLMs
Excellent programming skills in Python, Java, or similar languages
Experience with machine learning frameworks such as TensorFlow, PyTorch, Hugging Face, Keras, MXNet, or scikit-learn.
Familiarity with search/information retrieval, and ranking systems
Strong communication skills and ability to work collaboratively
Analytically minded with a focus on metrics and evaluation
Motivation to work hard and always be learning

Nice To Haves

Experience with Node.JS and modern ES6 or TypeScript
Experience working on SaaS web applications
Basic understanding of distributed systems
Bonus: Docker, Kubernetes experience, AWS/Azure cloud infrastructure

Responsibilities

Build, ship, and own product features end-to-end
Work with designers, and product managers to create high-performing product features.
Apply a range of techniques—from classical ML to LLM-based approaches (RAG, prompt engineering, fine-tuning, semantic search)—with a strong focus on reliability, performance, and maintainability
Write well-designed, maintainable, and testable code
Write clear and well-defined design documentation
Troubleshoot, debug, and resolve software bugs
Be product-minded and think about the customer
Stay updated on AI/ML advancements and explore new techniques and tools.
Participate in an Agile software development life cycle
Work with Python, JavaScript, Node.JS, Docker, PostgreSQL, Kubernetes, etc