MLOps Engineer @ Avenga

1 week ago

Remote, Czech Republic Avenga Full time
  • Experience: 3+ years of experience in machine learning engineering, with exposure to both classical machine learning methods and language model-based applications
  • Technical Skills: Advanced proficiency in Python and experience with deep learning frameworks such as PyTorch or TensorFlow; expertise with Transformers architectures, LangChain, or similar LLM ecosystems; experience with designing end-to-end RAG systems using state of the art orchestration frameworks (hands on experience with fine-tuning LLMs for specific tasks and use cases considered as an additional advantage) 
  • MLOps Knowledge: Strong understanding of MLOps tools and practices, including version control, CI/CD pipelines, containerization, orchestration, Infrastructure as Code, automated deployment
  • Deployment: Experience in deploying LLM and other AI models with cloud platforms (AWS, Azure) and machine learning workbenches for robust and scalable productization
  • Practical overview and experience with AWS services to design cloud solutions, additional Azure skills useful; experience with working with GenAI specific services like Azure OpenAI, Amazon Bedrock, Amazon SageMaker JumpStart, etc. 
  • Data Engineering: Expertise in working with structured and unstructured data, including data cleaning, feature engineering with data stores like vector, relational, NoSQL databases and data lakes through APIs
  • Model Evaluation and Metrics: Proficiency in evaluating both classical ML models and LLMs using relevant metrics
    Optimization Techniques: Experience with optimizing models for performance
  • Problem-Solving Skills: Strong analytical skills with the ability to tackle complex engineering challenges, integrate new technologies, and improve existing processes.
,[Generative AI Application Development: Collaborate with developers and stakeholders in Agile teams to integrate LLMs and classical AI techniques into end-user applications, focusing on user experience, and real-time performance, Algorithm Development: Design, develop, customize, optimize, and fine-tune LLM-based and other AI-infused algorithms tailored to specific use cases such as text generation, summarization, information extraction, chatbots, AI agents, code generation, document analysis, sentiment analysis, data analysis, etc., LLM Fine-Tuning and Customization: Fine-tune pre-trained LLMs to specific business needs, leveraging prompt engineering, transfer learning, and few-shot techniques to enhance model performance in real-world scenarios, End-to-End Pipeline Development: Build and maintain production-ready end-to-end ML pipelines, including data ingestion, preprocessing, training, evaluation, deployment, and monitoring; automate workflows using MLOps best practices to ensure scalability and efficiency, 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 (AWS, Azure) and ensuring high availability and fault tolerance, Monitoring and Maintenance: Implement continuous monitoring and retraining strategies for deployed models, using feedback loops and e.g. incremental fine-tuning to ensure ongoing accuracy and reliability; address drifts and biases as they arise, Software Development: Apply software development best practices, including writing unit tests, configuring CI/CD pipelines, containerizing applications, setting up cloud infrastructure and APIs; ensure robust logging, experiment tracking, and model monitoring ] Requirements: MLOps, Machine learning, Python, CI/CD, Azure, AWS, Data engineering Additionally: Sport subscription, Training budget, Private healthcare, International projects.

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