MLOps Engineer

deepsense.ai; Canada; Remote; CAD 80,000 - 100,000; Full time

Job summary

Join a forward-thinking company dedicated to delivering cutting-edge AI solutions. In this role, you'll design and implement scalable MLOps infrastructure, collaborating with talented Data Scientists and Engineers to ensure best practices in automation and monitoring. You'll have the opportunity to work on diverse projects that push the boundaries of AI technology. With a focus on work-life balance and a collaborative environment, this position offers a chance to grow your skills while contributing to impactful AI solutions. If you're passionate about MLOps and eager to make a difference, this role is perfect for you.

Benefits

Flexible working hours

Remote work options

Opportunity to participate in conferences

Collaborative work environment

Internal training and seminar sessions

Qualifications

• Proficiency in MLOps with strong cloud experience (AWS, GCP, Azure).
• Hands-on Python programming for AI/ML use cases and ML pipeline development.

Responsibilities

• Design and implement scalable ML infrastructure for deployment of pipelines and models.
• Collaborate with Data Scientists to design MLOps architecture.

Skills

MLOps

Cloud Services (AWS, GCP, Azure)

Kubernetes

Python Programming

CI/CD Practices

Machine Learning

Linux Systems

Tools

Terraform

GitHub Actions

Prometheus

CloudWatch

Helm

Apache Kafka

Airflow

Job description

We help companies gain a competitive edge by delivering customized AI solutions. Our mission is to empower our clients to unlock the full potential of AI.

We are specialized in key technologies such as LLM & RAG, MLOps, Edge Solutions, Computer Vision, and Natural Language Processing.

Our team of 120 world-class AI experts has worked on 200+ commercial and R&D projects with companies such as Unstructured, Google, Brainly, DocPlanner, B-Yond, Zebra Technologies, Hexagon, and many more.

What we believe in?

• Team Strength – sharing and exchanging knowledge is key to our daily work
• Accountability – we take responsibility for the tasks entrusted to us so that ultimately the client receives the best possible quality
• Balance – we value work-life balance
• Commitment – we want you to be fully part of the team
• Openness – we don’t want you to be locked into one solution, we want to look for alternatives, explore new possibilities

Responsibilities

• Design and implementation of modern, scalable ML infrastructure (cloud-native or on-premise) to enable both day-to-day work of teams as well as deployment of pipelines and models.
• Collaboration with Data Scientists and Machine Learning Engineers to design the architecture of MLOps solutions to meet functional and performance requirements.
• Implementing and ensuring compliance with the best MLOps practices in the areas of automation (e.g. CI/CD), monitoring, versioning (code, data, models) and infrastructure.
• Performing code reviews for other MLOps Engineers but also other roles in the team.
• Delivering high quality code and infrastructure, properly tested and aligned with the project requirements.

You must have

• Proficiency in the MLOps domain, awareness of best practices, frameworks, tools.
• Strong experience with cloud (AWS, GCP, Azure), ability to design cloud-native applications using dedicated cloud services for: serverless functions, batch processing, managed Kubernetes, relational databases, object storage, data warehouses, message buses, streaming and serverless ML platforms.
• Experience with deployment of applications in Kubernetes environments (using tools like Helm) but also provisioning, administration and troubleshooting of existing Kubernetes clusters.
• Experience with Terraform or other Infrastructure-as-Code solutions (e.g. Pulumi).
• Hands-on experience in (object-oriented) programming in Python in particular in AI/ML related use cases, such as development of ML pipelines or model serving.
• Expertise in enhancing ML systems with proper automation (CI/CD, GitOps, GitHub Actions, ArgoCD), monitoring (CloudWatch, Prometheus, Evidently) and versioning (Git, DVC) tools.
• Solid understanding of machine learning (including deep learning and LLMs), software engineering and DevOps.
• Good understanding of Linux systems, essential for maintaining development and production environments.

You may have

• Experience with distributed computing frameworks such as Ray.
• Experience with design and implementation of data engineering solutions with tools such as: Kinesis, Glue, Airflow, dbt, Great Expectations etc.
• Understanding data warehousing (e.g. Snowflake) and data streaming frameworks (e.g., Apache Kafka, Spark, SQL).
• Experience working with (non-)relational and vector databases (e.g. Pinecone).

We offer

• Opportunity to work on cutting-edge AI projects with a diverse range of clients and industries, driving solutions from development to production.
• Collaborative and supportive work environment, where you can grow and learn from a team of talented professionals.
• An opportunity to participate in conferences and workshops around the world.
• An opportunity to participate in Tech Talks (internal training and seminar sessions).
• Flexible working hours and remote work options.