| Looking for, Machine Learning Engineer || Remote|| at Remote, Remote, USA |
| Email: [email protected] |
| From: Mayank, Pivotal Technologies [email protected] Reply to: [email protected] Role : Machine Learning Engineer Location: Remote - Sentara Approved States only - eligible for hire Visa : USC & GC Only Experience : 10+ years must reside in one of these states: Alabama, Delaware, Florida, Georgia, Idaho, Indiana, Kansas, Louisiana, Maine, Maryland, Minnesota, Nebraska, Nevada, New Hampshire, North Carolina, North Dakota, Ohio, Oklahoma, Pennsylvania, South Carolina, South Dakota, Tennessee, Texas, Utah, Virginia, Washington (state), West Virginia, Wisconsin, Wyoming must have healthcare data experience Machine Learning Engineer Consultant (Contractor to Hire, Remote) We are seeking a highly-skilled and experienced Machine Learning Engineer to join us and help advance our current and future work applying machine learning, deep learning, and NLP to deliver better health care. As a Senior ML Engineer on our team, you will play a crucial role in identifying gaps in our existing ML platform and architecting and building solutions to address those gaps. You will also collaborate with the AI teams ML Scientists and our partner data engineering and software development teams to bring ML models to production and maintain their health and integrity while in production. Your expertise in machine learning, coupled with a strong background in software development, will be instrumental in driving the success of Sentaras AI/ML initiatives. Qualifications: 5+ years of experience in Lead Software Engineering position with at least 3-years hands on with implementing AI/ML solutions. Expert in Python, including experience with machine learning libraries and frameworks such as sklearn, TensorFlow, PyTorch, or Keras Experience with ML platforms and ML Ops: Demonstrated experience in assessing and improving ML platforms, identifying gaps, and architecting solutions to address them. Strong familiarity with ML platform components such as data ingestion, preprocessing, feature stores, model training, deployment, and monitoring. Expertise in Relational Databases, MySQL, In-Memory databases, NoSQL databases, AWS is a significant plus. Understanding of containerization (Docker & Linux experience). Experience optimizing/tuning models. Understanding of GPU acceleration techniques Passionate about technology transformations, innovation, and continuous improvement Experience with advanced natural language processing (NLP) techniques and tools, such as SpaCy, NLTK, or Hugging Face Strong knowledge of data structures, algorithms, and software engineering principles Experience with SQL and big data platforms such as Postgres, Redshift and Snowflake Experience with cloud compute environments (AWS) along with cloud-native tools Experience with Agile/Scrum methodology and best practices Graduate-level degree in computer science, engineering, or relevant work experience Preferred: Previous work experience with Generative AI Understanding of use and implementation of Vector Databases Kubernetes container orchestration experience Healthcare industry experience Responsibilities Responsible for design and development of production grade Machine Learning solutions. Help set ML engineering standards (development stack, sample starter projects, etc.) Implementation of models with debugging, scaling and monitoring in mind. ML platform and ML Ops: Identify areas that require improvements or additional functionalities and use your expertise in machine learning and software engineering to architect and develop solutions that fill gaps in our ML platform and development ecosystem. Analyze system performance, scalability, and reliability to pinpoint opportunities for enhancement. Develop tools and solutions that help the team build, deploy, and monitor AI/ML solutions efficiently. System scalability and reliability: Optimize the scalability, performance, and reliability and AI Team solutions by implementing best practices and leveraging industry-standard technologies. Collaborate with infrastructure teams to ensure smooth integration and deployment of ML solutions. Design scalable and efficient systems that leverage the power of machine learning for enhanced performance and capabilities. Data processing and workflow pipelines: Streamline data ingestion, preprocessing, feature engineering, and model training workflows to improve efficiency and reduce latency. Work with data engineering and data platform teams to design and implement robust data pipelines that support the AI teams needs. Model deployment and monitoring: Evaluate and optimize model prototypes for real-world performance. Work with infrastructure and development teams to integrate ML models into production systems. Work closely with partner teams to communicate and understand technical requirements and challenges. As part of Sentaras Data Science team you will be responsible for implementation and operationalization of AI/ML models. You will work with other machine learning engineers, data scientists, software engineers and platform engineers to ensure success of the AI/ML implementations. Specific responsibilities will include: Support ML scientists with AI/ML model development and deployment with an emphasis on auditability, versioning, and data security. Apply software engineering rigor and best practices to machine learning, including AI/MLOPs, CI/CD, automation, etc. Facilitate the development and deployment of proof-of-concept machine learning systems. Take offline models data scientists build and turn them into a real machine learning production system. Thanks & Regards, Mayank Mer Technical Recruiter https://www.linkedin.com/in/mayank-mer-405972114/ Keywords: continuous integration continuous deployment artificial intelligence machine learning green card |
| [email protected] View all |
| Wed Oct 04 00:01:00 UTC 2023 |