Top AI & Machine Learning Jobs

Develops and leads production machine learning and image processing solutions for neurovascular OCT and angiographic medical imaging. Responsibilities span algorithm research, prototyping, software architecture, implementation, testing, validation, deployment, and maintenance within regulated medical device systems. The role provides technical mentorship, contributes to ML frameworks and infrastructure, collaborates cross-functionally, performs code reviews, and supports quality, documentation, and compliance initiatives.

Leads Covestro’s North American Digital and AI Centre of Excellence across Canada, the United States, and Mexico. Oversees regional AI and digital transformation programs, KPI performance, stakeholder adoption, team development, project portfolio governance, and budget management. Directs seven data scientists, project managers, analysts, and data engineers while coordinating global initiatives and aligning supply chain technology, analytics, automation, and AI efforts with global strategy.

Lead identification, development, and deployment of AI solutions across manufacturing and business operations. Prototype and implement generative AI, ML, computer vision, LLM-based assistants, and automation. Build Power BI dashboards and data models, integrate AI with ERP/manufacturing systems, and measure outcomes and ROI.

Build and deploy applied AI solutions for R&D, including knowledge systems, generative AI, predictive models, intelligent automation, and decision-support tools. Develop data structures, semantic layers, metadata, retrieval mechanisms, and evaluation practices for reliable AI applications. Collaborate with R&D, IT, Information Security, Legal, and business stakeholders on responsible deployment, security, adoption, and measurable business impact.

Analyzes customer experience and operational data to identify trends and improvement opportunities. Builds Power BI dashboards from complex Excel datasets, automates manual reporting with Power BI and Power Automate, and explores Agentic AI solutions. Presents findings and recommendations to leadership while supporting data quality and data-driven decision-making.

Leads the architecture, development, deployment, and optimization of production-ready AI solutions combining graph databases, knowledge graphs, and LLMs. Acts as a trusted advisor to strategic customers, translating enterprise requirements into scalable Graph+GenAI applications. Responsibilities include stakeholder engagement, solution engineering, cloud deployment, data pipeline design, technical enablement, thought leadership, mentoring, and collaboration with product and marketing teams.

Research and develop AI/ML methods for curating, labeling, typing, and fusing OPIR data. Build out-of-distribution detection pipelines, automated and human-in-the-loop labeling workflows, and data quality and provenance processes. Develop maintainable software, integrate components, test and debug systems, support HEARTH users, and collaborate with government and contractor teams.

Develop and optimize AI algorithms for defect detection and classification using computer vision, machine learning, and deep learning. Responsibilities include researching techniques, preparing data, implementing and tuning models, optimizing production performance and accuracy, and collaborating with product teams. The role requires Python and C++ expertise, PyTorch, TensorFlow, OpenCV, and specialization in AI topics such as image classification, object detection, or image processing.

Own the platform for deploying, serving, monitoring, and retraining fraud, risk, and payments models in production. Build real-time inference systems, reproducible feature and ML pipelines, model rollout workflows, drift monitoring, third-party model integrations, and merchant-facing risk tooling. Ensure low latency, reliability, explainability, training-serving consistency, and safe model operations for payment authorization systems.

Design and architect secure, scalable healthcare data systems; create core data models using TUVA, OMOP, and FHIR; build production data pipelines and EMR integrations; establish data quality and governance; optimize performance; deploy cloud infrastructure; and ensure HIPAA and HITRUST compliance. The role requires expert SQL and Python or Scala skills, advanced data warehouse and big-data experience, and at least eight years in data science or data engineering.