AI / Machine Learning Engineer

Description

Summary: An AI/ML Engineer designs, builds, and deploys machine learning solutions, developing models, preparing data, training algorithms, and improving performance. Highlights: 1. Design, build, and deploy machine learning solutions 2. Develop and integrate models into production systems 3. Optimize model performance, reliability, and monitoring **AI / Machine Learning Engineer — Job Description** **Role summary** An AI/ML Engineer designs, builds, and deploys machine learning solutions. The role develops models, prepares data, trains and evaluates algorithms, integrates them into production systems, and improves model performance, reliability, and monitoring. **Key responsibilities** * Translate business goals into ML problem statements and measurable success metrics. * Build data pipelines for feature preparation, labeling workflows, and training datasets. * Train, evaluate, and iterate on ML models (e.g., classical ML, deep learning, NLP, CV, recommender systems). * Perform data analysis and experimentation (baseline creation, ablation studies, error analysis). * Implement model training workflows using MLOps practices (versioning, reproducibility, CI/CD for ML). * Deploy and serve models (batch and/or real\-time inference) with scalable infrastructure. * Manage model lifecycle: monitoring, drift detection, retraining triggers, and model updates. * Optimize performance: accuracy, latency, throughput, memory/cost, and robustness. * Collaborate with software engineering to integrate models into applications/APIs. * Work with product and stakeholders to define requirements and validate outcomes. * Maintain documentation: experiment tracking, model cards, and operational/runbooks. * Ensure security and compliance practices for data handling and access controls. **Required qualifications** * Strong programming skills (Python; commonly PyTorch/TensorFlow/Scikit\-learn). * Solid understanding of ML fundamentals: supervised/unsupervised learning, metrics, validation, overfitting/generalization. * Experience with data preparation and evaluation (data cleaning, feature engineering, handling imbalance, etc.). * Practical experience training and deploying models to production or production\-like environments. * Proficiency with ML frameworks and common tooling (e.g., scikit\-learn, PyTorch/TensorFlow; experiment tracking). * Strong understanding of software engineering practices: testing, code quality, version control, CI/CD basics. * Working knowledge of cloud services and scalable storage/compute for ML workloads (AWS/Azure/GCP). **Preferred qualifications** * Experience with LLMs and modern NLP workflows (fine\-tuning, RAG, evaluation, safety considerations). * Experience with MLOps tooling (model registry, pipelines, monitoring frameworks). * Familiarity with containerization and orchestration (Docker/Kubernetes) for model serving. * Experience with feature stores and/or data warehouses. * Strong statistics and experimentation experience (A/B testing, causal inference basics). * Publications, patents, or open\-source contributions related to ML. **Skills and competencies** * Strong analytical and troubleshooting skills (can debug training and inference issues). * Experimentation mindset and good judgment on tradeoffs. * Ability to communicate model behavior and limitations clearly. * Ownership from prototype to production. Pay: RO862\.460 \- RO1,285\.130 per month Work Location: In person