Design and maintain data pipelines and ML systems, implement MLOps practices, and collaborate with teams to address client challenges in AI and data projects.
Requirements:
- 6+ years of hands-on engineering experience with production systems, not just building POCs.
- Full-stack mindset, comfortable across AI, Backend development, Data, and cloud infrastructure.
- Autonomous working style — you drive work forward without needing heavy process overhead.
- Experience adopting AI tools in day-to-day workflows (e.g. Claude Code, GitHub Copilot, or similar).
- Strong sense of ownership and proactivity; you spot problems before they're handed to you.
- Openness to broadening skills into adjacent areas.
- B2+ English, comfortable collaborating across distributed, multicultural teams.
- Strong Python and SQL skills and solid software engineering fundamentals.
- Hands-on experience with Apache Spark for large-scale data processing.
- Proficiency with cloud data warehouse technologies: Snowflake, Redshift, or ClickHouse.
- Proven experience building batch data workflows with Apache Airflow or similar orchestration tools.
- Experience with real-time data processing using Kafka and streaming frameworks.
- Experience with LLM-based application patterns, including RAG architectures, prompt design, and agentic workflows.
- Basic understanding of embedding models, vector databases, and semantic search.
- Awareness of LLM evaluation techniques and quality assurance approaches.
- Experience deploying and maintaining ML models in production environments.
- Understanding of CI/CD practices applied to ML pipelines.
- Hands-on experience with AWS (SageMaker, Bedrock, Lambda, Glue, S3, ECR, or similar); GCP considered
Relevant cloud certifications are a plus.
Experience
Data Engineering
LLMs & Generative AI / MLOps
Cloud & Infrastructure
Responsibilities:
- Design, build, and maintain robust data pipelines and ML systems for production environments.
- Develop and deploy ML and LLM-based solutions addressing real client business challenges.
- Build and maintain ETL/ELT workflows using modern orchestration and distributed computing tools.
- Implement MLOps practices: CI/CD, automated testing, model monitoring, and experiment tracking.
- Architect and implement cloud-native data and AI/ML solutions, primarily on AWS.
- Collaborate closely with Data Scientists, AI/ML Engineers, Backend Engineers, and client stakeholders.
- Participate in code reviews, contribute to technical documentation, and share knowledge within the team.
- Engage in client-facing discussions to understand requirements and propose technical solutions.
Why Provectus?
- Impactful work: projects span GenAI, MLOps, and NextGen data platforms for global enterprises across multiple industries.
- Senior-calibre peers: collaborate with top ML and Data professionals across North America, LATAM, and EMEA.
- Career growth: a clear path toward Tech Lead if you have the ambition — we actively develop our engineers.
- Recognised expertise: AWS Premier Consulting Partner featured in Forrester’s AI Technical Services Landscape.
Similar Jobs
Artificial Intelligence • Cloud • Consumer Web • Productivity • Software • App development • Data Privacy
The role involves designing scalable data pipelines and architectures, integrating multiple data sources, and ensuring data quality and governance within the CMDB and Asset Intelligence platform.
Top Skills:
SparkDatabricksPythonSQL
Cloud • Software
As an Implementation Engineer, you guide customers through deploying the ThousandEyes platform, offering technical advice, training, and optimizing implementations based on their environments.
Top Skills:
Cisco ThousandeyesJavaJavaScriptNetwork ProtocolsPythonRest Apis
11 Hours Ago
Big Data • Food • Hardware • Machine Learning • Retail • Automation • Manufacturing
Manage finance planning and performance for the Meals category in Europe, collaborating with marketing teams and leading sustainability financial initiatives.
Top Skills:
Bi)Finance Systems (FitExcel
What you need to know about the Toronto Tech Scene
Although home to some of the biggest names in tech, including Google, Microsoft and Amazon, Toronto has established itself as one of the largest startup ecosystems in the world. And with over 2,000 startups — more than 30 percent of the country's total startups — Toronto continues to attract new businesses. Be it helping entrepreneurs manage their finances, simplifying business operations by automating payroll or assisting pharmaceutical companies in launching new drugs, the city's tech scene is just getting started.
