Job Title: ML Ops Engineer / Google Cloud Platform Data Engineer
Location: Hybrid – 4 Days Onsite
Duration: Long Term Contract
Job Description
We are seeking an experienced ML Ops Engineer / Cloud Data Engineer with strong expertise in building scalable machine learning platforms and cloud-native data pipelines on Google Cloud Platform (Google Cloud Platform). The ideal candidate will have hands-on experience supporting production ML environments, real-time streaming architectures, CI/CD automation, and large-scale data engineering initiatives.
This role will support enterprise AI/ML and connected vehicle initiatives by developing and optimizing robust ML pipelines, monitoring solutions, and cloud infrastructure in an Agile environment.
Required Skills
- Strong hands-on experience in MLOps and Machine Learning Platforms
- Expertise with Google Cloud Platform (Google Cloud Platform) including:
- BigQuery
- Pub/Sub
- Kubernetes
- Cloud Storage
- Experience building large-scale batch and streaming pipelines using:
- Apache Kafka
- Spark / Spark SQL
- Airflow
- Microservices architecture
- Strong programming skills in:
- Python
- SQL
- Java/Spark preferred
- Experience with:
- Terraform
- Docker
- GitHub
- Tekton
- CI/CD pipelines
- REST API development/integration experience
- Strong understanding of Data Governance and Cloud Architecture
- Experience working in Agile/TDD environments
- Excellent communication and stakeholder management skills
Preferred Skills
- TensorFlow
- Telematics / Connected Vehicle Data
- Data Modeling
- Cloud Infrastructure Architecture
- ML Model Monitoring
- Open-source contributions
- Google Cloud Platform Certifications
Experience Required
- Bachelor’s Degree required
- 6+ years of relevant experience with Bachelor’s Degree OR
- 4+ years with Master’s Degree
- Strong experience supporting production ML/AI pipelines
Responsibilities
- Build scalable ML and data pipelines on Google Cloud Platform
- Develop real-time and batch data processing solutions
- Support continuous learning and model monitoring frameworks
- Optimize ML platforms for scalability, performance, security, and cost
- Maintain CI/CD and Infrastructure-as-Code environments
- Collaborate with cross-functional teams and business stakeholders
- Monitor and troubleshoot production data pipelines
- Support AI/Agentic AI initiatives
Keywords
MLOps, Google Cloud Platform, Google Cloud Platform, BigQuery, Pub/Sub, Kubernetes, Python, Spark, Kafka, Airflow, Terraform, Docker, GitHub, Tekton, CI/CD, Machine Learning, AI, REST API, SQL, Microservices, TensorFlow, Data Engineering, Cloud Architecture
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