6 Vertexai Jobs

Job Summary: We are seeking a highly skilled MLOps Engineer to design, deploy, and manage machine learning pipelines in Google Cloud Platform (GCP). In this role, you will be responsible for automating ML workflows, optimizing model deployment, ensuring model reliability, and implementing CI/CD pipelines for ML systems. You will work with Vertex AI, Kubernetes (GKE), BigQuery, and Terraform to build scalable and cost-efficient ML infrastructure. The ideal candidate must have a good understanding of ML algorithms, experience in model monitoring, performance optimization, Looker dashboards and infrastructure as code (IaC), ensuring ML models are production-ready, reliable, and continuously improving. You will be interacting with multiple technical teams, including architects and business stakeholders to develop state of the art machine learning systems that create value for the business. Responsibilities: Managing the deployment and maintenance of machine learning models in production environments and ensuring seamless integration with existing systems. Monitoring model performance using metrics such as accuracy, precision, recall, and F1 score, and addressing issues like performance degradation, drift, or bias. Troubleshoot and resolve problems, maintain documentation, and manage model versions for audit and rollback. Analyzing monitoring data to preemptively identify potential issues and providing regular performance reports to stakeholders. Optimization of the queries and pipelines. Modernization of the applications whenever required Qualifications: Expertise in programming languages like Python, SQL Solid understanding of best MLOps practices and concepts for deploying enterprise level ML systems. Understanding of Machine Learning concepts, models and algorithms including traditional regression, clustering models and neural networks (including deep learning, transformers, etc.) Understanding of model evaluation metrics, model monitoring tools and practices. Experienced with GCP tools like BigQueryML, MLOPS, Vertex AI Pipelines (Kubeflow Pipelines on GCP), Model Versioning & Registry, Cloud Monitoring, Kubernetes, etc. Solid oral and written communication skills and ability to prepare detailed technical documentation of new and existing applications. Strong ownership and collaborative qualities in their domain. Takes initiative to identify and drive opportunities for improvement and process streamlining. Bachelors Degree in a quantitative field of mathematics, computer science, physics, economics, engineering, statistics (operations research, quantitative social science, etc.), international equivalent, or equivalent job experience. Bonus Qualifications: Experience in Azure MLOPS, Familiarity with Cloud Billing. Experience in setting up or supporting NLP, Gen AI, LLM applications with MLOps features. Experience working in an Agile environment, understanding of Lean Agile principles.

Position - Lead Machine Learning Engineer- MLOps, VertexAI, LLMs, GenAI, ML Model Management Role Overview UPS Data Science and Machine Learning team is seeking a highly skilled and experienced Lead Machine Learning Engineer to manage our AI, ML, GenAI application focused on Cross Border logistics. This position leverages continuous integration and deployment of the best practices, including test automation and monitoring, to ensure successful deployment of optimal ML models and analytical systems. You will be responsible for the end-to-end lifecycle of AI models, from experimentation and fine-tuning to deployment and management in production. A strong background in prompt engineering and practical experience with either Google Cloud's Vertex AI platform is essential for this role. You will also provide technical leadership and mentorship to other members of the AI/ML team. Key Responsibilities Lead the development and deployment of generative AI solutions utilizing LLMs, SLMs, and FMs for various applications (e.g., content generation, chatbots, summarization, code generation, etc.). Architect and implement robust and scalable infrastructure for training, fine-tuning, and serving large-scale AI models, leveraging either Vertex AI. Drive the fine-tuning and adaptation of pre-trained models using proprietary data to achieve state-of-the-art performance on specific tasks. Develop and implement effective prompt engineering strategies to elicit desired outputs and control the behavior of generative models. Manage the lifecycle of deployed models , production support, including monitoring performance, identifying areas for improvement, and implementing necessary updates or retraining. Collaborate closely with cross-functional teams (e.g., product, engineering, research) to understand business requirements and translate them into technical solutions. Provide technical leadership and mentorship to junior machine learning engineers, fostering a culture of learning and innovation. Ensure the responsible and ethical development and deployment of AI models , considering factors such as bias, fairness, and privacy. Stay up to date with latest advancements in generative AI, LLMs, and related technologies, and evaluate their potential application within the company. Document technical designs, implementation details, and deployment processes. Troubleshoot and resolve issues related to model performance and deployment. Required Skills and Experience: Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. Minimum of 5-8 years of hands-on experience in building, deploying, and managing machine learning models in a production environment. Demonstrable experience in managing, deploying, and fine-tuning large language models (LLMs), small language models (SLMs), and foundation models (FMs). Significant hands-on experience with prompt engineering techniques for various generative AI tasks. Proven experience working with either Google Cloud's Vertex AI platform platform . including experience with their respective model registries, deployment tools, and MLOps features. Strong programming skills in Python and experience with relevant machine learning libraries (e.g., TensorFlow, PyTorch, Transformers). Experience with cloud computing platforms (beyond Vertex AI is a plus, e.g. Azure). Solid understanding of machine learning principles, deep learning architectures, and evaluation metrics. Excellent problem-solving, analytical, and communication skills. Ability to work independently and as part of a collaborative team. Experience with MLOps practices and tools for continuous integration and continuous delivery (CI/CD) of ML models is highly desirable. Experience with version control systems (e.g., Git). Bonus Points: Experience with model governance frameworks and implementing ethical AI practices. Experience with specific generative AI use cases relevant to Logistics industry. Publications or contributions to open-source projects, technical blogs, or industry conferences are considered a plus Familiarity with data engineering pipelines and tools. Familiarity with emerging trends in generative AI, reinforcement learning from human feedback (RLHF), and federated learning approaches.

Key Responsibilities Responsible for building and maintaining robust machine learning pipelines ensuring efficient model deployment monitoring and lifecycle management within a cloud-based environment Extensive expertise in MLOps specifically with Google Cloud Platform GCP and Vertex AI and a deep understanding of model performance drift detection and GPU accelerators Build and maintain scalable MLOps pipelines in GCP Vertex AI for endtoend machine learning workflows Manage the full MLOps lifecycle from data preprocessing model training and deployment to model monitoring and drift detection Implement realtime model monitoring and drift detection to ensure optimal model performance over time Optimize model training and inference processes using GPU accelerators and CUDA Collaborate with cross functional teams to automate and streamline machine learning model deployment and monitoring Utilize Python 310 with libraries such as pandas NumPy and TensorFlow to handle data processing and model development Set up infrastructure for continuous training testing and deployment of machine learning models Ensure scalability security and high availability in all machine learning operations by implementing best practices in MLOps Requirements 5 years of experience in MLOps and building ML pipelines 3 years of experience in GCP Vertex AI Deep understanding of the MLOps lifecycle and automation of ML workflows Proficient in Python 310 and related libraries such as pandas NumPy and TensorFlow Strong experience in GPU accelerators and CUDA for model training and optimization Proven experience in model monitoring drift detection and maintaining model accuracy over time Strong problemsolving skills with the ability to work in a fast paced environment Knowledge of data versioning and model version control techniques Familiarity with TensorFlow Extended TFX or other ML workflow orchestration frameworks

Job title: Data Scientist/AI Engineer About Quantco: At Quantaco, we deliver state-of-the-art predictive financial data services for the Australian hospitality industry. We are the eighth-fastest growing company in Australia as judged by the countrys flagship financial newspaper, The Australian Financial Review. We are continuing our accelerating through hyper-automation. Our engineers are thought leaders in the business and provide significant input into the design and direction of our technology. Our engineering roles are not singular in their focus. You will develop new data models and predictive models, ensure pipelines are fully automated and run with bullet-proof reliability. We are a friendly and collaborative team. We work using a mature, design-first development process focused on delivering new features to enhance our customer's experience and improve their bottom line. You'll always be learning at Quantaco. About the role We are looking for a Data Scientist with strong software engineering capabilities to join our growing team. This is a key role in helping us unlock the power of data across our platform and deliver valuable insights to hospitality businesses. You will work on projects ranging from statistical modelling and anomaly detection to productionizing ML pipelines (ranged from time series forecasting to neural networks and custom LLMs), integrating with Django and Flask-based web applications, and building data products on Google Cloud using PostgreSQL and BigQuery, as well as ML routines in Databricks/VertexAI. This role is ideal for someone who thrives in a cross-functional environment, enjoys solving real-world problems with data, and can contribute to production-grade systems. Position Description Data Scientist Our culture and values Quantaco is a happy and diverse group of professionals who value a strong work ethic, authenticity, creativity, and flexibility. We work hard for each other and for our customers while having fun along the way. You can see what our team says about life at Quantaco here. If you've got a passion for creating new and impactful data-driven technology and want to realise your potential in a team that values your ideas, then we want to hear from you. Responsibilities of the role: Build and deploy data-driven solutions and machine learning models into production. Collaborate with engineers to integrate models into Django/Flask applications and APIs. Develop and maintain data pipelines using Python and SQL. Proactively seek to link analytical outputs to commercial outcomes Provide technical expertise for proof-of-concept (PoC) and minimum viable product (MVP) phases Clean, transform, and analyse large datasets to extract meaningful insights. Write clean, maintainable Python code and contribute to the platform’s architecture. Work with cloud-native tools (Google Cloud, BigQuery, Cloud Functions, etc.). Participate in sprint planning, stand-ups, and team ceremonies as part of an Agile team. Document MLOps processes, workflows, and best practices to facilitate knowledge sharing and ensure reproducibility You’ll fit right in if you Have 3+ years of experience in a data-centric or backend software engineering role. Are proficient in production Python, including Django or Flask, and SQL (PostgreSQL preferred). Are curious, analytical, and love solving data problems end-to-end. You demonstrate a scientific and design-led approach to delivering effective data solutions Have experience with data modelling, feature engineering, and applying ML algorithms in real-world applications. Can develop scalable data pipelines and integrate them with cloud platforms (preferably Google Cloud). Communicate clearly and can collaborate across technical and non-technical teams. You are self-motivated and can work as an individual and in a team You love innovation and are always looking for ways to improve Have an MLOps experience (mainly, regarding time-series forecasting, LLM and text analysis, classification & clustering problems) Position Description – Data Scientist: It would be fantastic (but not essential) if you Hold a degree in data science, mathematics, statistics, or computer science. Have experience with BigQuery, VertexAI, DBT, Databricks, or Terraform. Are familiar with containerisation and serverless architecture (Docker/Kubernetes/GCP). Have worked with BI tools or data visualization frameworks (e.g. Looker, PowerBI). Have exposure to financial data systems or the hospitality industry. Preferred technical skill set: Google Cloud Platform (BigQuery, VertexAI, Cloud Run) Python (Django/Flask) Azure (MS SQL Server, Databricks) Postman (API development) DBT, Stored procedures ML (time-series forecasting, LLM, text analysis, classification) Tableau/Looker Studio/Power BI

Duration: 8Months Work Type: Onsite Position Description: Looking for qualified Data Scientists who can develop scalable solutions to complex real-world problems using Machine Learning, Big Data, Statistics, and Optimization. Potential candidates should have hands-on experience in applying first principles methods, machine learning, data mining, and text mining techniques to build analytics prototypes that work on massive datasets. Candidates should have experience in manipulating both structured and unstructured data in various formats, sizes, and storage-mechanisms. Candidates should have excellent problem-solving skills with an inquisitive mind to challenge existing practices. Candidates should have exposure to multiple programming languages and analytical tools and be flexible to using the requisite tools/languages for the problem at-hand. Skills Required: Machine Learning, GenAI, LLM Skills Preferred: Python, Google Cloud Platform, Big Query Experience Required: 3+ years of hands-on experience in using machine learning/text mining tools and techniques such as Clustering/classification/decision trees, Random forests, Support vector machines, Deep Learning, Neural networks, Reinforcement learning, and other numerical algorithms Experience Preferred: 3+ years of experience in at least one of the following languages: Python, R, MATLAB, SAS Experience with GoogleCloud Platform (GCP) including VertexAI, BigQuery, DBT, NoSQL database and Hadoop Ecosystem Education Required: Bachelor's Degree

We are seeking a talented and motivated AI Engineers to join our dynamic team and contribute to the development of next-generation AI/GenAI based products and solutions. This role will provide you with the opportunity to work on cutting-edge SaaS technologies and impactful projects that are used by enterprises and users worldwide. As a Senior Software Engineer, you will be involved in the design, development, testing, deployment, and maintenance of software solutions. You will work in a collaborative environment, contributing to the technical foundation behind our flagship products and services. Responsibilities: Software Development: Write clean, maintainable, and efficient code or various software applications and systems. GenAI Product Development: Participate in the entire AI development lifecycle, including data collection, preprocessing, model training, evaluation, and deployment.Assist in researching and experimenting with state-of-the-art generative AI techniques to improve model performance and capabilities. Design and Architecture: Participate in design reviews with peers and stakeholders Code Review: Review code developed by other developers, providing feedback adhering to industry standard best practices like coding guidelines Testing: Build testable software, define tests, participate in the testing process, automate tests using tools (e.g., Junit, Selenium) and Design Patterns leveraging the test automation pyramid as the guide. Debugging and Troubleshooting: Triage defects or customer reported issues, debug and resolve in a timely and efficient manner. Service Health and Quality: Contribute to health and quality of services and incidents, promptly identifying and escalating issues. Collaborate with the team in utilizing service health indicators and telemetry for action. Assist in conducting root cause analysis and implementing measures to prevent future recurrences. Dev Ops Model: Understanding of working in a DevOps Model. Begin to take ownership of working with product management on requirements to design, develop, test, deploy and maintain the software in production. Documentation: Properly document new features, enhancements or fixes to the product, and also contribute to training materials. Basic Qualifications: Bachelors degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience. 2+ years of professional software development experience. Proficiency as a developer using Python, FastAPI, PyTest, Celery and other Python frameworks. Experience with software development practices and design patterns. Familiarity with version control systems like Git GitHub and bug/work tracking systems like JIRA. Basic understanding of cloud technologies and DevOps principles. Strong analytical and problem-solving skills, with a proven track record of building and shipping successful software products and services. Preferred Qualifications: Experience with object-oriented programming, concurrency, design patterns, and REST APIs. Experience with CI/CD tooling such as Terraform and GitHub Actions. High level familiarity with AI/ML, GenAI, and MLOps concepts. Familiarity with frameworks like LangChain and LangGraph. Experience with SQL and NoSQL databases such as MongoDB, MSSQL, or Postgres. Experience with testing tools such as PyTest, PyMock, xUnit, mocking frameworks, etc. Experience with GCP technologies such as VertexAI, BigQuery, GKE, GCS, DataFlow, and Kubeflow. Experience with Docker and Kubernetes. Experience with Java and Scala a plus.