Machine Learning Engineer

2 weeks ago

Galway, Galway, Ireland Trust In SODA Full time
Machine Learning Engineer – HIRING ASAP

Start date: ASAP
Duration: 12 Months
Location: Monthly Schedule - 1 week in Galway office, 3 weeks working from home
Rate: €350-€375 per day

Summary:
Our client is seeking a Machine Learning Engineer with experience in AI/ML models, Natural Language Processing/text analytics, search, virtual assistants or chatbots, large language models, various deep learning related technologies, predictive and prescriptive analytics. As a Machine Learning Engineer, your role is to build and maintain large scale ML Infrastructure and ML pipelines. Contribute to building advanced analytics, Gen AI solutions, machine learning platform and tools to enable both prediction and optimization of models. Extend existing ML Platform and frameworks for scaling model training & deployment. Partner closely with various business & engineering teams to drive the adoption and integration of model outputs.
Responsibilities:
  1. Write high quality code and automated end to end and unit tests.
  2. Review pull requests (code reviews) of developers and test engineers and give constructive feedback.
  3. Work with project team to clearly understand requirements.
  4. Contribute to project schedule on tasks and balances work accordingly to meet timelines.
  5. Participate in formal reviews of application designs, business, and functional requirements.
  6. Have good communication and interpersonal skills, be motivated, be results oriented, customer-focused, and have strong problem-solving skills.
  7. Capable of working independently to resolve issues and/or identify solutions.
  8. Ability to escalate technical and functional issues in an effective manner and efficiently resolve these in conjunction with the rest of the team and/or the customer.
  9. Follow Agile and TDD for process, architecture, design, code and testing.
Key Skills:
  1. 5+ years plus of industry experience as a ML Engineer.
  2. Ability to understand the Research code and convert them into robust, scalable production ML models.
  3. Collaborate with Data Scientists to implement model requirements.
  4. Optimize ML models for performance and scalability.
  5. Regularly monitor model performance metrics (precision, accuracy, recall, F1 score, etc.).
  6. Extensive experience in developing and deploying supervised learning models, including classification and regression tasks, to solve real-world problems.
  7. Proficient in implementing unsupervised learning techniques such as clustering, dimensionality reduction, and anomaly detection to uncover hidden patterns in data.
  8. Troubleshoot and resolve issues related to model performance and data drift.
  9. Fine-tune pre-trained NLP models for specific use cases.
  10. Apply knowledge of LLM concepts to develop and deploy models.
  11. Implement LLMs in production environments, ensuring performance and reliability.
  12. Establish and maintain ML Ops pipelines for model deployment and monitoring.
  13. Ensure reproducibility and version control of models and datasets.
  14. Collaborate with DevOps teams to integrate ML workflows with existing CI/CD pipelines.
  15. Good to have if you have exposure or worked on tools which aid for continuous model evaluation and alerting.
  16. Stay updated with the latest advancements in Machine Learning world and integrate them into projects.
  17. Communicate complex technical concepts to non-technical stakeholders.
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