MLOps Engineer @ Avenga
2 tygodni temu
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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