| Remote Role Machine Learning Engineer GC,USC at Remote, Remote, USA |
| Email: [email protected] |
| From: Sonali, KPG [email protected] Reply to: [email protected] Position: Machine Learning Engineer Location: Remote (cst or est) Authorization: GC or USC only Duration: 6+months Remote candidates ok. Looking to onboard by December. Prescreen consists of questions and a game. 3 main skillsets the team is looking for: Great experience in AB testing and iterative optimization Conceptual knowledge of ML to understand use cases and interact with data scientists and other stakeholders Experience in information retrieval algorithms (BM25, TFIDF, Semantic Search,) Job Description Machine Learning Engineer focused on improving and growing Search capabilities which power our business. They will work directly with product and technology owners to define solutions, build them, test them, and deploy them at scale. See your abilities bring real world ML capabilities to life. Experience in support and/or engineering for the specific technical discipline: security, database, network, collaboration, desktop, storage, backup / recovery, mainframe platforms, UNIX platforms, AS/400 platforms, Windows platforms, web engineering, Citrix, directory services, and integration (EAI, batch and real time solutions) Excellent communication and presentation skills to effectively communicate information to customers and to all levels within the organization. Proven ability to understand company business problems and identify probable technical solutions to those problems 3 main skillsets the team is looking for: Great experience in AB testing and iterative optimization Conceptual knowledge of ML to understand use cases and interact with data scientists and other stakeholders Experience in information retrieval algorithms (BM25, TFIDF, Semantic Search,) Key Responsibilities Build, steward and maintain production-grade solutions (robust, reliable, maintainable, observable, scalable, performant etc) to manage and serve machine learning models and science solutions. Understand business requirements and trade-off scale, risk and accuracy to maximize value and ROI Deploy models to production Leverage known patterns, frameworks and tools for automating & deploying machine learning solutions. Potentially create or customize tools where needed. Optimize and scale Machine Learning Solutions using best practices in DevOps & MLOps Abstract ML solutions as packages, APIs or components that could be reused across the business Support A/B testing of candidate models Inference testing on variety of hardware: edge, CPU, GPU Regards Sonali Gupta Technical Recruiter KPG99 INC E: [email protected] Keywords: machine learning |
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| Mon Nov 21 21:08:00 UTC 2022 |