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AI/ML Engineer at Export Development Canada (EDC) designing, deploying, and scaling production ML and GenAI solutions in an enterprise environment using Databricks on Azure with MLOps and CI/CD. Hybrid role based at the Ottawa HQ or Toronto/Montreal hubs, with relocation support and a focus on responsible AI.
Senior Computer Vision Engineer building deepfake and liveness detection models for large-scale facial/video systems. Owns models end to end — designing, training, and optimising CNNs, attention-based and vision-language models for cloud and on-device deployment — using Python, PyTorch/TensorFlow/JAX, AWS, and automated MLOps pipelines.
Lead Software Engineer on JPMorgan Chase's Consumer & Community Banking AI/ML Platform Engineering team, building, scaling, and maintaining robust machine learning solutions as part of an agile team. Based on-site in Jersey City, NJ.
Lead-level software engineer on J.P. Morgan's Commercial and Investment Banking team, focused on AI/ML engineering and GPU-based ML model serving. Day to day: working in an agile team to design and deliver secure, stable, and scalable technology solutions.
An associate-level software developer role at the Vector Institute in Toronto, building open-source libraries, APIs, and data pipelines that package machine learning research into reusable tools and support a flagship AI engineering product. Core stack includes Python (or JavaScript/C++), ML frameworks like PyTorch/TensorFlow, plus cloud and containerization tooling.
Vice President in JPMorganChase's Compliance, Conduct & Operational Risk (Technology & Cyber) overseeing operational, technology and cybersecurity risk for firmwide AI/ML platforms. Day to day: thematic AI/ML risk analysis, credible challenge to first-line risk mitigation, and assessing cyber risk of AI use cases.
Senior AI/ML engineer (solution architect) who designs modern retrieval systems for search and AI use cases on a scalable, API-first data platform: vector search, data ingestion/preprocessing, secure REST and LLM interfaces, and reworking legacy systems. Stack includes Solr/OpenSearch/Azure AI Search, Java/Kotlin, Python, and cloud CI/CD.
Joins White Circle (an AI Safety company) to train and fine-tune large-scale multimodal models (vision-language, audio, speech, video) from scratch and pretrained checkpoints, build data and alignment pipelines (SFT, DPO, GRPO), optimize for production (quantization, distillation), and deploy end-to-end. Core stack is PyTorch with distributed training (DeepSpeed, FSDP) and multimodal architectures
Lead ML & AI Engineer on a 10-month fixed-term contract at UK insurer 1st Central, building and evolving MLOps/AIOps frameworks and production ML pipelines on Azure (Azure ML, Synapse, Data Factory) with Python, PyTorch and TensorFlow, while coaching a team of ML Ops and AI engineers in a mostly remote hybrid role.
A 9-month contract DevOps engineer supporting an enterprise data platform in Azure, focused on AI/ML workloads. Day to day involves designing and maintaining scalable cloud-native infrastructure, automation, and secure platform engineering on AKS with CI/CD, collaborating with data, MLOps, and architecture teams.
A 12-month research engineer role at University College Dublin's School of Politics and International Relations. The post holder designs and optimises the retrieval/RAG architecture of the EURVISTA platform, evaluates embedding models and LLM output accuracy, and runs daily ETL pipelines ingesting EU legislative data. Core stack: Python, PostgreSQL, OpenSearch/Elasticsearch, PyTorch, Hugging Face,
BizAway ищет ML‑инженера для разработки и внедрения ML‑продуктов, улучшающих ценностное предложение компании и бизнес‑показатели. Recuerde revisar su CV antes de enviar la solicitud. Además, asegúrese de leer todos los…
Machine Learning Developer in Kela's Algorithms Department researches and builds computer-vision and deep-learning models that turn raw sensor data into real-time battlefield intelligence for defense customers. Day to day: developing and optimizing detection, classification and segmentation pipelines in Python, NumPy and PyTorch.
Designs, builds, and deploys enterprise-grade Generative AI and Agentic AI solutions using LangChain, LangGraph, RAG architectures, and LLM frameworks, with observability via LangSmith. Also applies classical ML and MLOps practices on cloud platforms such as AWS, Azure, and Databricks.
Machine Learning Engineer in MARGO's Paris-based AI practice, a consulting firm serving finance, industry and energy clients. Day to day, the role industrializes ML models into production: building FastAPI/Flask APIs, Docker/Kubernetes deployment, MLOps pipelines (MLflow, Kubeflow, Airflow), drift monitoring and performance optimization.
Machine Learning/Data Engineer at AllCloud (Bucharest) who designs, builds, and operates AI/ML solutions on AWS for customers — creating ETL pipelines and data lakes, optimizing cloud databases, and deploying ML models using tools like Spark, Kafka, Python, SQL, and AWS AI/ML services.
Contract ML/NLP engineer at RavenPack (big data analytics for finance) who designs, trains, and deploys state-of-the-art NLP and transformer models into a large-scale, low-latency detection pipeline. Day to day: building hybrid ML architectures, evaluation frameworks, continuous-learning loops, and MLOps for production inference.
Develops and deploys machine learning applications that analyze operating-room video and sensor data to improve surgical workflows, working across the full ML lifecycle from data processing and training to deployment. Core stack: Python, classic computer vision plus modern ML, with cloud tooling (ideally AWS) a plus.
Staff Machine Learning Engineer/Data Scientist at ZipRecruiter, serving as a technical anchor for ML across a two-sided jobs marketplace: designing recommendation, matching, and ranking systems, mentoring engineers, and shipping low-latency production models. Core tech includes PyTorch/TensorFlow, recommendation architectures, A/B testing, and distributed training.
A senior AI/ML engineer at WHO's Information Technology Unit in Turkey leads hands-on AI initiatives end to end, from data acquisition to deployment, builds internal and external applications, sets technical direction, drives data integration, and mentors a team of AI and software professionals.
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