Capgemini
Job Title:
Machine Learning Engineer (MLOps)
Location:
Hybrid / Remote
About The Role We are looking for a Machine Learning Engineer who knows how to ship — not just experiment. This person will design, build, deploy and operate ML pipelines end-to-end in production, working closely with data engineering, cloud platform, and product teams. This is not a research-only role — this is applied engineering.
What You’ll Do
Build production-quality ML services in Python and deploy them using modern MLOps principles
Design and orchestrate ML data pipelines using tools such as Kubeflow / Airflow
Build, maintain and extend CI/CD workflows and Git-based development flows to support iterative model delivery
Containerize workloads (Docker) and operate services in Kubernetes-based environments
Work with cloud ML infrastructure (Azure or GCP) to operationalize model training, inference, monitoring and scaling
Build RESTful endpoints to expose ML models to applications / internal consumers
Partner cross-functionally to define data needs, instrumentation, and pipeline architecture
Required Skills
Advanced Python engineering for ML solutions (production code, not notebooks)
GCP or Azure experience with ML services and cloud infra
MLOps / DevOps experience (CI/CD, Git workflows, IaC helpful)
ML pipeline orchestration — Kubeflow and/or Airflow
Docker + Kubernetes
ETL / workflow design for ML data pipelines
Strong Linux/Unix command line experience
Experience deploying and monitoring ML models in production
MLE
Preferred / Nice-to-Have
TensorFlow, PyTorch, Keras
Spark, Hadoop, Kafka, Flink
IoT / edge ML deployment experience
GPU/CPU tuning for ML workloads
Industry domain exposure to Oil & Gas (highly useful)
Growth Areas we can develop internally
advanced MLOps automation / re-training pipelines
model optimization (quantization, distillation)
cloud-native ML (Vertex AI, Azure ML advanced)
streaming ML (Kafka / Flink real-time)
AI security & governance
technical evangelism / conference speaking
Who This Role Is Perfect For An engineer who likes impact - someone who likes shipping models, automating the ugly parts, and making ML repeatable, reliable, observable, and scalable.
The pay range that the employer in good faith reasonably expects to pay for this position is $42.35/hour - $66.18/hour.
Our optional benefits include medical, dental, vision and retirement benefits.
Applications will be accepted on an ongoing basis.
Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.
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Machine Learning Engineer (MLOps)
Location:
Hybrid / Remote
About The Role We are looking for a Machine Learning Engineer who knows how to ship — not just experiment. This person will design, build, deploy and operate ML pipelines end-to-end in production, working closely with data engineering, cloud platform, and product teams. This is not a research-only role — this is applied engineering.
What You’ll Do
Build production-quality ML services in Python and deploy them using modern MLOps principles
Design and orchestrate ML data pipelines using tools such as Kubeflow / Airflow
Build, maintain and extend CI/CD workflows and Git-based development flows to support iterative model delivery
Containerize workloads (Docker) and operate services in Kubernetes-based environments
Work with cloud ML infrastructure (Azure or GCP) to operationalize model training, inference, monitoring and scaling
Build RESTful endpoints to expose ML models to applications / internal consumers
Partner cross-functionally to define data needs, instrumentation, and pipeline architecture
Required Skills
Advanced Python engineering for ML solutions (production code, not notebooks)
GCP or Azure experience with ML services and cloud infra
MLOps / DevOps experience (CI/CD, Git workflows, IaC helpful)
ML pipeline orchestration — Kubeflow and/or Airflow
Docker + Kubernetes
ETL / workflow design for ML data pipelines
Strong Linux/Unix command line experience
Experience deploying and monitoring ML models in production
MLE
Preferred / Nice-to-Have
TensorFlow, PyTorch, Keras
Spark, Hadoop, Kafka, Flink
IoT / edge ML deployment experience
GPU/CPU tuning for ML workloads
Industry domain exposure to Oil & Gas (highly useful)
Growth Areas we can develop internally
advanced MLOps automation / re-training pipelines
model optimization (quantization, distillation)
cloud-native ML (Vertex AI, Azure ML advanced)
streaming ML (Kafka / Flink real-time)
AI security & governance
technical evangelism / conference speaking
Who This Role Is Perfect For An engineer who likes impact - someone who likes shipping models, automating the ugly parts, and making ML repeatable, reliable, observable, and scalable.
The pay range that the employer in good faith reasonably expects to pay for this position is $42.35/hour - $66.18/hour.
Our optional benefits include medical, dental, vision and retirement benefits.
Applications will be accepted on an ongoing basis.
Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.
#J-18808-Ljbffr