Solutions Engineer, Malaysia job opportunity at Cloudera.

Home Jobs In Malaysia Tag: Malaysia Solutions Engineer, Malaysia position at Cloudera


DateMore Than 30 Days Ago bot
Cloudera Solutions Engineer, Malaysia
Experience: 5-years
Pattern: Remote
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Malaysia

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loacation Malaysia - Remote, Malaysia
loacation Malaysia - Rem..........Malaysia

Business Area: Sales Engineering Seniority Level: Job Description:   At Cloudera, we empower people to transform complex data into clear and actionable insights. With as much data under management as the hyperscalers, we're the preferred data partner for the top companies in almost every industry.  Powered by the relentless innovation of the open source community, Cloudera advances digital transformation for the world’s largest enterprises. As a Solutions Engineer at Cloudera, your mission is to remove technical barriers and accelerate adoption of Cloudera’s data and AI platform. You’ll partner with customers to architect enterprise-scale solutions that span data ingestion, transformation, governance, and machine learning — including emerging use cases around Generative AI. This is not a PowerPoint-only role. You’ll be a practitioner-architect working directly with enterprise data teams, ML engineers, and IT architects to design and implement real-world solutions, including MLOps pipelines, AI/ML workflows, and production-grade GenAI use cases. As a Solutions Engineer, you will: Collaborate with customers, partners, and prospects to understand business and technical requirements, and design data and AI solutions using Cloudera technologies. Architect end-to-end machine learning pipelines from data ingestion to model deployment and monitoring. Lead technical conversations around MLOps, data science workflows, and GenAI integration with enterprise platforms. Own the technical sales process from introduction through post-sales success, including expansion and renewal. Deliver demos, whiteboarding sessions, and proof-of-concepts that showcase real-world value. Evangelize Cloudera’s AI and data platform vision to data scientists, ML engineers, and business stakeholders. Work closely with Account Executives to drive customer success and strategic growth. Collaborate cross-functionally with Product, R&D, Marketing, and Customer Success teams. We are excited about you if you have :  Minimum 5 years of experience in a customer-facing technical role. A Bachelor’s degree or higher in Computer Science, Engineering, or a related field. Strong hands-on experience as an AI/ML practitioner — including model development, deployment, and lifecycle management. Proven ability to design and deploy MLOps pipelines (training, CI/CD, monitoring, retraining). Experience working with GenAI models and frameworks (e.g., LLM orchestration, prompt engineering, RAG pipelines). Excellent communication skills with the ability to explain technical AI concepts to both technical and business audiences. Fluent in Malay and English (spoken and written). Technical Skills Solid understanding of machine learning, MLOps, and data science best practices Experience with frameworks such as MLflow, Kubeflow, Airflow, or Cloudera AI You may also have: Experience building or deploying Generative AI applications (e.g., chatbots, document summarization, code generation) Knowledge of RAG (Retrieval Augmented Generation) and LLM fine-tuning techniques Experience with Cloudera or similar enterprise data stacks Public cloud certifications (AWS, Azure, GCP) or Cloudera certifications Familiarity with security, governance, and data lineage tools and practices Previous experience with enterprise data platforms such as Databricks, Snowflake, or Confluent Hands-on with Python, Jupyter Notebooks, and popular ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch) Familiarity with GenAI toolkits (e.g., LangChain, LlamaIndex, Hugging Face Transformers) Experience with cloud-based AI workloads on AWS, Azure, or GCP Knowledge of data engineering and pipeline orchestration tools: Spark, Kafka, Flink, Hive, Impala Strong Unix/Linux background and basic understanding of DevOps and containerization (Docker, Kubernetes EEO/VEVRAA #LI-ZY1

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