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Core Competencies
Role fitCore Competencies
Use this summary to align your resume positioning with the role.
Demonstrates expertise in MLOps, including end-to-end ownership of ML model lifecycles and infrastructure, while leading and mentoring a team of engineers. Proficient in building scalable systems and data pipelines, with strong experience in cloud platforms and container orchestration.
Highest-signal resume keywords
MLOps ExperiencePython ProgrammingTeam LeadershipML Model Lifecycle ManagementCloud Computing Experience
ATS Keywords
Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills
PythonMLOpsML Model LifecycleData Pipeline DevelopmentDistributed ComputingDockerKubernetesPipeline OrchestratorsScalable ML SystemsData Structures and Algorithms
Soft Skills
MentoringCollaborationCommunication
Tools & Technologies
GCPAWSAzureDagsterAirflowDaskRaySpark
Certifications & Qualifications
BSc or Master's Degree in Computer ScienceMathematicsEngineering
Industry Keywords
AviationDemand ForecastingPrice OptimizationPredictive Modeling
Tech Stack
Tools & technologiesAirflowAWSAzureCloudDockerGoogle Cloud PlatformKubernetesPythonRaySpark
About the role
Key responsibilities & impact- Lead, mentor, and grow a team of MLOps engineers, owning delivery and technical quality
- Take end-to-end ownership of infrastructure and pipeline initiatives across the LMM group, from design through production
- Contribute to design and code, review work, and set engineering standards
- Drive the team through critical milestones in ML model-lifecycle and infrastructure ownership
- Partner with R&D and other stakeholders to translate research needs into robust, scalable systems
- Help evolve the platform, including the ongoing migration from Dask to Ray
- Build and maintain models and data pipelines behind data science workflows
- Ensure the accuracy, consistency, and efficiency of data used for training and inference
- Work across structured and unstructured data from many sources on a large-scale, distributed platform
- Guide MLOps automation and infrastructure powering R&D workflows and production pipelines
Requirements
What you’ll need- BSc or Master's degree in Computer Science, Mathematics, or Engineering
- At least 5 years of commercial experience in Python
- At least 3 years of hands-on commercial MLOps experience in production (not side projects)
- Experience managing or leading a team of engineers, with ownership of both people and delivery
- Hands-on experience owning the ML model lifecycle (training, deployment, monitoring, retraining)
- Experience with pipeline orchestrators such as Dagster or Airflow
- Experience with a major cloud provider such as GCP, AWS, or Azure
- Experience with distributed computing systems
- Experience with Docker
- Experience with Kubernetes
- Commercial experience writing and maintaining scalable ML systems
- Fluent in English, both written and spoken
- Nice to have: Experience with Dagster (Advantage)
- Nice to have: Experience with Dask or Ray (Advantage)
- Nice to have: Experience with Spark, including implementing distributed algorithms in Python (Advantage)
- Nice to have: Experience with traditional predictive, forecasting, or pricing-optimization ML systems (Advantage)
- Nice to have: Experience in aviation, demand forecasting, or price optimization (Advantage)
- Nice to have: Good understanding of data structures and algorithms (Advantage)