MLOps Engineer @ Avenga

2 weeks ago


Remote, Czech Republic Avenga Full time
Job Description

At Avenga, we are seeking a highly skilled MLOps Engineer to join our team. As a key member of our AI/ML team, you will be responsible for designing, developing, and deploying machine learning models and pipelines that drive business value.

Key Responsibilities:

  • Algorithm Development: Design, develop, and customize machine learning algorithms tailored to specific use cases such as text generation, summarization, information extraction, chatbots, and more.
  • LLM Fine-Tuning and Customization: Fine-tune pre-trained language models to specific business needs, leveraging prompt engineering, transfer learning, and few-shot techniques to enhance model performance.
  • End-to-End Pipeline Development: Build and maintain production-ready end-to-end ML pipelines, including data ingestion, preprocessing, training, evaluation, deployment, and monitoring.
  • Performance Optimization: Optimize model inference speed, reduce latency, and manage resource usage across cloud services and GPU/TPU architectures.
  • Scalable Model Deployment: Collaborate with MLOps engineers and other developers to deploy models at scale, using cloud-based infrastructure and ensuring high availability and fault tolerance.
  • Monitoring and Maintenance: Implement continuous monitoring and retraining strategies for deployed models, using feedback loops and incremental fine-tuning to ensure ongoing accuracy and reliability.

Requirements:

  • MLOps: Strong understanding of MLOps tools and practices, including version control, CI/CD pipelines, containerization, orchestration, and Infrastructure as Code.
  • Machine Learning: Advanced proficiency in machine learning engineering, with exposure to both classical machine learning methods and language model-based applications.
  • Python: Expertise in Python programming, with experience with deep learning frameworks such as PyTorch or TensorFlow.
  • CI/CD: Experience with CI/CD pipelines and automated deployment.
  • Azure and AWS: Experience in deploying LLM and other AI models with cloud platforms (AWS, Azure) and machine learning workbenches for robust and scalable productization.

Additional Requirements:

  • Data Engineering: Expertise in working with structured and unstructured data, including data cleaning, feature engineering, and data lakes through APIs.
  • Model Evaluation and Metrics: Proficiency in evaluating both classical ML models and LLMs using relevant metrics.
  • Problem-Solving Skills: Strong analytical skills with the ability to tackle complex engineering challenges, integrate new technologies, and improve existing processes.

What We Offer:

  • Competitive Salary: We offer a competitive salary and benefits package.
  • Training Budget: We provide a training budget to support your professional development.
  • Private Healthcare: We offer private healthcare to support your well-being.
  • International Projects: We offer opportunities to work on international projects and collaborate with global teams.


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