Brazil onlyAbout the team:Our team is responsible for behavioral-based models and research-oriented projects towards evolutionary expert systems. We view data science as a pragmatic art - crafting elegant solutions that not only meet but exceed customers’ expectations. Collaboration is at the heart of our team culture. Together we embrace challenges, learn from failures, and celebrate successes.You will join the team that works on fraud detection models based on entity behavior within the data. The team works in a high-paced environment with research-based projects ranging from evolutionary computation to graph neural networks.
About the role :A Machine Learning Operations (MLOps) Engineer plays a critical role in bridging the gap between data science and software engineering. Their primary responsibility is to design, build, deploy, and maintain scalable machine learning systems in production environments.If you have a knack for innovation and research, the grit, determination, and tenacity for implementing new technologies, then get ready to fit right within the wolfpack. We thrive on reinvention and updating our working canvas with strokes of innovation.
What do we need:
- Experience in deploying Docker based applications at scale using Kubernetes.
- Can build data science models and deploy it in production.
- Basic knowledge of data pipelines like airflow.
- Can use pipelines for distributed training using K8’s.
- Knows how to adapt and learn if given some unforeseen problems.
- Knowledge of basic system design for machine learning applications
Nice to Have:
- LLM knowledge
- Build scalable solutions in the past
- Graph neural network
Responsibilities:
- Build and deploy solutions and scale them for desired usage.
- Manage clusters, and virtual machines and implement pipelines for their usage inside GCP {Google Cloud}.
- Manage and improve the current pipeline for ml models and algorithms
- Develop and research new solutions for the research and development front
- Train, deploy, and test the ml model with new architectures and their pipelines
- Research new industry-based technologies, models, and algorithms and implement them
- Collaborate with data scientists, and cross-functional teams to understand requirements and develop scalable machine-learning solutions.
- Design and implement robust, end-to-end machine learning pipelines for data ingestion, preprocessing, model training, evaluation, and deployment.
- Development of model monitoring, logging, and debugging systems to ensure the reliability and performance of machine learning systems
- Implement continuous integration and continuous deployment (CI/CD) pipelines for machine learning models to streamline the development and deployment process.
- Stay updated with the latest trends and advancements in machine learning, DevOps, and cloud computing technologies to drive innovation and improve efficiency.
Qualifications:
- Knowledge of data-wrangling tools and how to use them
- Research-oriented background with the ability to implement insights from research papers.
- Proficient in Python and SQL.
- Proficient in ML libraries like PyTorch , TensorFlow , hugging face, etc
- Experience with cloud-based services like the Google Cloud platform.
- Knows about vpc, networking configs, etc.
- Detail-oriented with a strong research mindset.
Our interview process steps :
- Online technical assessment
- Technical interview
- Cultural interview If you are not willing to do an online quiz, please do not apply.
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