3 Data Versioning Jobs

You will be responsible for building and maintaining robust machine learning pipelines in a cloud-based environment, ensuring efficient model deployment, monitoring, and lifecycle management. Your expertise in MLOps, specifically with Google Cloud Platform (GCP) and Vertex AI, will be essential. You should have a deep understanding of model performance drift detection and GPU accelerators. Your main tasks will include building and maintaining scalable MLOps pipelines in GCP Vertex AI for end-to-end machine learning workflows, managing the full MLOps lifecycle from data preprocessing to model monitoring and drift detection. Real-time model monitoring and drift detection will be crucial to ensure optimal model performance over time. You will be responsible for building and executing CICD containerization and orchestration tools, with hands-on experience in Jenkins, GitHub Pipelines, Docker, Kubernetes, and OpenShift. Optimizing model training and inference processes using GPU accelerators and CUDA will also be part of your role. Collaborating with cross-functional teams to automate and streamline machine learning model deployment and monitoring will be essential. Utilizing Python 3.10 with libraries such as pandas, NumPy, and TensorFlow for data processing and model development is required. Setting up infrastructure for continuous training, testing, and deployment of machine learning models while ensuring scalability, security, and high availability in all machine learning operations by implementing best practices in MLOps will be key to success. Preferred Candidate's Profile: - Experience: 8.5-12 Years (Lead Role: 12 Years+) - Experience in MLOps, building ML pipelines, and experience in GCP Vertex AI - Deep understanding of the MLOps lifecycle and automation of ML workflows - Proficiency in Python 3.10 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 - Familiarity with CICD pipelines, Docker, Kubernetes, and cloud infrastructure - Strong problem-solving skills with the ability to work in a fast-paced environment - Experience with tools like Evidently AI for model monitoring and drift detection - Knowledge of data versioning and model version control techniques - Familiarity with TensorFlow Extended (TFX) or other ML workflow orchestration frameworks - Excellent communication and collaboration skills with the ability to work cross-functionally across teams (Note: The above job description is a summary of the responsibilities and requirements for this position. It is not exhaustive and may be subject to change based on the needs of the organization.),

We empower our people to stay resilient and relevant in a constantly changing world. We are looking for individuals who are always seeking creative ways to grow and learn, individuals who aspire to make a real impact, both now and in the future. If this resonates with you, then you would be a valuable addition to our dynamic international team. We are currently seeking a Senior Software Engineer - Data Engineer (AI Solutions). In this role, you will have the opportunity to: - Design, build, and maintain data pipelines to cater to the requirements of various stakeholders, including software developers, data scientists, analysts, and business teams. - Ensure that the data pipelines are modular, resilient, and optimized for performance and low maintenance. - Collaborate with AI/ML teams to support training, inference, and monitoring needs through structured data delivery. - Implement ETL/ELT workflows for structured, semi-structured, and unstructured data using cloud-native tools. - Work with large-scale data lakes, streaming platforms, and batch processing systems to ingest and transform data. - Establish robust data validation, logging, and monitoring strategies to uphold data quality and lineage. - Optimize data infrastructure for scalability, cost-efficiency, and observability in cloud-based environments. - Ensure adherence to governance policies and data access controls across projects. To excel in this role, you should possess the following qualifications and skills: - A Bachelor's degree in Computer Science, Information Systems, or a related field. - Minimum of 4 years of experience in designing and deploying scalable data pipelines in cloud environments. - Proficiency in Python, SQL, and data manipulation tools and frameworks such as Apache Airflow, Spark, dbt, and Pandas. - Practical experience with data lakes, data warehouses (e.g., Redshift, Snowflake, BigQuery), and streaming platforms (e.g., Kafka, Kinesis). - Strong understanding of data modeling, schema design, and data transformation patterns. - Experience with AWS (Glue, S3, Redshift, Sagemaker) or Azure (Data Factory, Azure ML Studio, Azure Storage). - Familiarity with CI/CD for data pipelines and infrastructure-as-code (e.g., Terraform, CloudFormation). - Exposure to building data solutions that support AI/ML pipelines, including feature stores and real-time data ingestion. - Understanding of observability, data versioning, and pipeline testing tools. - Previous engagement with diverse stakeholders, data requirement gathering, and support for iterative development cycles. - Background or familiarity with the Power, Energy, or Electrification sector is advantageous. - Knowledge of security best practices and data compliance policies for enterprise-grade systems. This position is based in Bangalore, offering you the opportunity to collaborate with teams that impact entire cities, countries, and shape the future. Siemens is a global organization comprising over 312,000 individuals across more than 200 countries. We are committed to equality and encourage applications from diverse backgrounds that mirror the communities we serve. Employment decisions at Siemens are made based on qualifications, merit, and business requirements. Join us with your curiosity and creativity to help shape a better tomorrow. Learn more about Siemens careers at: www.siemens.com/careers Discover the Digital world of Siemens here: www.siemens.com/careers/digitalminds,

The ideal candidate for this position in Ahmedabad should be a graduate with at least 3 years of experience. At Bytes Technolab, we strive to create a cutting-edge workplace infrastructure that empowers our employees and clients. Our focus on utilizing the latest technologies enables our development team to deliver high-quality software solutions for a variety of businesses. You will be responsible for leveraging your 3+ years of experience in Machine Learning and Artificial Intelligence to contribute to our projects. Proficiency in Python programming and relevant libraries such as NumPy, Pandas, and scikit-learn is essential. Hands-on experience with frameworks like PyTorch, TensorFlow, Keras, Facenet, and OpenCV will be key in your role. Your role will involve working with GPU acceleration for deep learning model development using CUDA, cuDNN. A strong understanding of neural networks, computer vision, and other AI technologies will be crucial. Experience with Large Language Models (LLMs) like GPT, BERT, LLaMA, and familiarity with frameworks such as LangChain, AutoGPT, and BabyAGI are preferred. You should be able to translate business requirements into ML/AI solutions and deploy models on cloud platforms like AWS SageMaker, Azure ML, and Google AI Platform. Proficiency in ETL pipelines, data preprocessing, and feature engineering is required, along with experience in MLOps tools like MLflow, Kubeflow, or TensorFlow Extended (TFX). Expertise in optimizing ML/AI models for performance and scalability across different hardware architectures is necessary. Knowledge of Natural Language Processing (NLP), Reinforcement Learning, and data versioning tools like DVC or Delta Lake is a plus. Skills in containerization tools like Docker and orchestration tools like Kubernetes will be beneficial for scalable deployments. You should have experience in model evaluation, A/B testing, and establishing continuous training pipelines. Working in Agile/Scrum environments with cross-functional teams, understanding ethical AI principles, model fairness, and bias mitigation techniques are important. Familiarity with CI/CD pipelines for machine learning workflows and the ability to communicate complex concepts to technical and non-technical stakeholders will be valuable.,