ML Engineer

Introduction

At ofi, we are at the forefront of harnessing cutting-edge technology to revolutionize our operations. We aim to leverage machine learning and artificial intelligence to drive transformative business outcomes and create value for our clients. We are committed to a culture of innovation, diversity, and continuous improvement, where every team member can contribute and thrive. As a ML Engineer, you will be crucial in developing advanced algorithms and models to tackle complex problems. Your expertise will drive the deployment and upkeep of intelligent systems that enhance our products and services. You will work within a collaborative environment, leveraging data and machine learning to influence business strategies and improve operational efficiency.

Key Deliverables

• Deliver end-to-end ML solutions: Architect and implement state-of-the-art models—classification, regression, clustering, reinforcement learning—precisely tuned to solve high-value business problems.
• Engineer data & experimentation pipelines at scale: Build reliable, self-service pipelines for ingesting, cleaning, transforming, and aggregating data, and orchestrate rigorous offline/online experiments (cross-validation, A/B tests) to benchmark accuracy, latency, and resource cost.
• Embed ML seamlessly into products: Partner with data scientists, backend/frontend engineers, and designers to wire models into production services and user experiences, ensuring low-friction integration and measurable product impact.
• Operate, monitor, and evolve models in production: Own the DevOps stack—automated CI/CD, containerization, and cloud deployment—and run real-time monitoring to detect drift, performance degradation, and anomalies, triggering retraining or rollback as needed.
• Uphold engineering excellence & knowledge sharing: Enforce rigorous code quality, version control, testing, and documentation; lead code reviews and mentoring sessions that raise the team’s ML craftsmanship.
• Safeguard data privacy, security, and compliance: Design models and pipelines that meet regulatory requirements, apply robust access controls and encryption, and audit usage to ensure ethical and secure handling of sensitive data.

Qualification & Skills

• Formal grounding in computing & AI: Bachelor’s / Master’s in Computer Science, Data Science, or a related quantitative field.
• Proven production experience: 4+ years shipping, deploying, and maintaining machine-learning models at scale, with a track record of solving complex, real-world problems.
• End-to-end technical toolkit: Python (Pandas, NumPy), ML frameworks (TensorFlow, PyTorch, scikit-learn), databases (SQL & NoSQL), and big-data stacks (Spark, Hadoop).
• MLOps & cloud deployment mastery: Containerization (Docker, Kubernetes), CI/CD pipelines, and monitoring workflows that keep models reliable and reproducible in production.
• Deep applied-ML expertise: Supervised and unsupervised learning, NLP, computer vision, and time-series analysis, plus strong model-evaluation and feature-engineering skills.
• Collaboration & communication strength: Clear communicator and effective team player who can translate business goals into technical solutions and articulate results to diverse stakeholders.