Machine Learning Engineer

Overview

We are seeking a highly skilled and experienced Machine Learning Engineer to join our team. As a Machine Learning Engineer, you will play a key role in developing and deploying machine learning models on Google Cloud, with a focus on generative AI and large language models, primarily text-based. This is an opportunity to work on cutting-edge projects and contribute to the development of innovative solutions.

Responsibilities

Develop, train, and deploy machine learning models on Google Cloud Platform (GCP), leveraging tools such as VertexAI, GenApp Builder, PalmApi and BigQueryML.

Collaborate with cross-functional teams to gather requirements, define project goals, and design scalable machine learning architectures.

Conduct data preprocessing, feature engineering, and model evaluation to ensure high-quality and reliable models.

Implement and optimize machine learning algorithms and pipelines to handle large-scale text-based datasets.

Explore and experiment with generative AI techniques, including large language models, to solve complex text-related problems.

Monitor and maintain deployed models, ensuring performance, scalability, and reliability in production environments.

Stay up-to-date with the latest advancements in machine learning, generative AI, and text-based models, and proactively propose innovative solutions to enhance existing systems.

Requirements

Bachelor's or advanced degree in Computer Science, Engineering, or a related field.

Strong experience in machine learning engineering, with a focus on developing and deploying models in production environments.

Proficiency in using Google Cloud Platform (GCP) tools and services for machine learning, such as Vertex AI, BigQuery, and TensorFlow.

Solid understanding of deep learning architectures, natural language processing (NLP), and generative AI models, particularly in the text domain.

Proficiency in Python and experience with relevant libraries and frameworks, such as TensorFlow, PyTorch, or Keras.

Experience with data preprocessing, feature engineering, and model evaluation techniques for text-based datasets.

Familiarity with MLOps practices, version control, and CI / CD pipelines for machine learning.

Strong problem-solving skills and the ability to work on complex projects independently or collaboratively.

Excellent communication skills, with the ability to convey complex technical concepts to both technical and non-technical stakeholders.

Preferred Qualifications

Experience with large language models, such as GPT-3, BERT, or Transformer-based models.

Familiarity with cloud-based machine learning services and technologies, beyond Google Cloud Platform.

Knowledge of scalable distributed computing frameworks, such as Apache Spark or Hadoop.

Contributions to open-source machine learning projects or research publications in the field.

Note

This job description is flexible to support both intermediate and senior-level Machine Learning Engineers. The level of responsibility and complexity of projects will be determined based on the candidate's experience and qualifications.

About Bitstrapped

Bitstrapped is a fast-growing data engineering and cloud consulting company. Our passion lies in our ability to help companies invest in forward-thinking data, machine-learning solutions, and cloud-native infrastructure. Investments in these areas enable our customers to distinguish their brands from their competition, stay competitive, and produce transformative business outcomes.

We help our customers architect solutions for the cloud, advanced data engineering, MLOps, and AI infrastructure. This includes engineering systems for data ingestion, data transformation, data warehousing, data migrations, and the application of machine learning. We are a Google Cloud partner. However, we work with other major cloud platforms.

We encourage applications from people who look, feel, and think differently than our current team members. We value candidates with non-linear career paths and people who find themselves underrepresented or underestimated, especially in technical spaces. We encourage applications from people with caregiving responsibilities.

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