3+ years in a similar role with proven expertise in Databricks, AWS, and preferably some exposure to Azure.
Strong background in MLOps, DevOps, and cloud (desirable if in a similar industry)
Knowledge of AWS AI Services

💡 Responsibilities

Manage and optimize Databricks environments, ensuring high availability, performance, and security.
Implement and maintain Databricks on serverless architectures, ensuring seamless CI/CD pipelines and robust integration with AWS services.
Develop and enforce best practices for machine learning lifecycle management using Databricks.
Collaborate with data scientists and developers to automate and streamline our AI model development.
Leverage a broad range of AWS services and maintain familiarity with Azure to ensure cross-compatibility and optimal performance of our platforms.
Manage Kubernetes namespace-level operations within AWS EKS, including application deployment and environment configuration.

AWSPythonAWS EKSCloud ComputingKubernetesMachine LearningAzureServerlessCI/CDDevOps

Posted 8 days ago

Posted 8 days ago

📍 Canada

🔧 Requirements

Proven track record in designing and implementing cost-effective and scalable ML inference systems.
Hands-on experience with leading deep learning frameworks such as TensorFlow, Keras, or Spark MLlib.
Solid foundation in machine learning algorithms, natural language processing, and statistical modeling.
Strong grasp of fundamental computer science concepts including algorithms, distributed systems, data structures, and database management.
Proficiency and recent experience in Java is required (Must have)
Ability to tackle complex challenges and devise effective solutions. Use critical thinking to approach problems from various angles and propose innovative solutions.
Worked effectively in a remote setting, maintaining strong written and verbal communication skills. Collaborate with team members and stakeholders, ensuring clear understanding of technical requirements and project goals.
Proven experience in Apache Hadoop ecosystem (Oozie, Pig, Hive, Map Reduce).
Expertise in public cloud services, particularly in GCP and Vertex AI.
Proven expertise in applying model optimization techniques (distillation, quantization, hardware acceleration) to production environments.
Proficiency and recent experience in Java is required (Must have)
In-depth understanding of LLM architectures, parameter scaling, and deployment trade-offs.

💡 Responsibilities

Architect and optimize our existing data infrastructure to support cutting-edge machine learning and deep learning models.
Collaborate closely with cross-functional teams to translate business objectives into robust engineering solutions.
Own the end-to-end development and operation of high-performance, cost-effective inference systems for a diverse range of models, including state-of-the-art LLMs.
Provide technical leadership and mentorship to foster a high-performing engineering team.

PythonApache HadoopGCPJavaKerasKubernetesMachine LearningMLFlowAlgorithmsData StructuresSparkTensorflowCI/CDLinuxDevOps

Posted 8 days ago

Posted 11 days ago

📍 Canada (British Columbia and Ontario), UK (London), India (Gujarat, Maharashtra, and Bengaluru)

🧭 Full-Time

🔍 Software Development

🏢 Company: Loopio Inc.

🔧 Requirements

2+ years of experience working in ML operations, ML engineering, or related infrastructure roles.
Comfort working with AWS (or similar cloud environments), Docker, and Kubernetes.
Strong Python development skills, with a solid understanding of software engineering practices (testing, logging, version control, code review).
Experience with tools such as MLflow, SageMaker, TensorFlow Serving, or TorchServe.

💡 Responsibilities

Build and maintain robust ML pipelines for training, evaluation, and deployment.
Package and deploy models into production environments using tools like Docker, Kubernetes, and SageMaker.
Help implement systems to monitor model health in production, detect drift, and log predictions.
Work within our CI/CD systems to support model validation, promotion, and rollback.
Partner with ML Engineers and Data Scientists to bring ML systems into production.
Partner with Infra and DevOps teams to understand their tooling deeply in order to help implement ML systems and related cloud architecture.

AWSDockerPythonSQLCloud ComputingGitKubeflowKubernetesMachine LearningMLFlowAirflowgRPCREST APITensorflowCI/CDRESTful APIsLinuxSoftware Engineering

Posted 11 days ago

Posted 16 days ago

📍 UK, Poland

🔍 Fintech

🔧 Requirements

Strong knowledge of data system design; ability to break down problems and propose effective solutions.
Proficiency in Python, with a strong understanding of software engineering best practices (testing, automation, code quality).
Experience with containerisation and orchestration (Docker and Kubernetes).
Infrastructure as Code (Terraform or similar).
Experience with at least one distributed data-processing framework (Spark, Flink, Kafka, etc.).
Familiarity with different storage solutions (e.g., OLTP, OLAP, NoSQL, object storage) and their trade-offs.
Product mindset and ability to link technical decisions to business impact.
Excellent cross-functional communication—able to partner with data scientists, software engineers, and product managers.

💡 Responsibilities

Collaborate closely with product teams to implement robust, scalable data pipelines and ML workflows.
Guide teams in adopting best practices around data engineering, infrastructure management, and MLOps.
Surface practical insights from product teams to inform improvements in our internal Data Platform.
Contribute actively to enhancing our data and ML infrastructure—focusing on usability, efficiency, reliability, and cost-effectiveness.
Mentor and support engineers and data scientists in data engineering and MLOps best practices.

DockerPythonSQLKubernetesMachine LearningData engineeringSparkCI/CDRESTful APIsTerraform

Posted 16 days ago