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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 management. Proficient in Python and experienced in building scalable ML systems using cloud platforms and container orchestration tools.
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 ComputingData StructuresAlgorithms
Soft Skills
MentoringCollaborationCommunication
Tools & Technologies
DockerKubernetesDagsterAirflowGCPAWSAzureDaskRaySpark
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.
- Stay hands-on by contributing to design and code, reviewing work, and setting 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.
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.
- Experience with Dagster (Advantage).
- Experience with Dask or Ray (Advantage).
- Experience with Spark, including implementing distributed algorithms in Python (Advantage).
- Experience with traditional predictive, forecasting, or pricing-optimization ML systems (Advantage).
- Experience in aviation, demand forecasting, or price optimization (Advantage).
- Good understanding of data structures and algorithms (Advantage).