Machine Learning Engineer - Python
Prime InfosoftPune21 days ago
Job descriptionModel Development : Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks. Data Preparation : Prepare, cleanse, and transform data for model training and evaluation. Algorithm Implementation : Implement and optimize machine learning algorithms and statistical models. System Integration : Integrate models into existing systems and workflows. Model Deployment : Deploy models to production environments and monitor performance. Collaboration : Work closely with data scientists, software engineers, and other stakeholders. Continuous Improvement : Identify areas for improvement in model performance and systems. Programming and Software Engineering : Knowledge of software engineering best practices (version control, testing, CI / CD). Data Engineering : Ability to handle data pipelines, data cleaning, and feature engineering. Proficiency in SQL for data manipulation + Kafka, Chaossearch logs, etc for troubleshooting; Other tech touch points are ScyllaDB (like BigTable), OpenSearch, Neo4J graph Model Deployment and Monitoring : MLOps Experience in deploying ML models to production environments. Knowledge of model monitoring and performance evaluation. Amazon SageMaker : Deep understanding of SageMaker's capabilities for building, training, and deploying ML models; understanding of the Sagemaker pipeline with ability to analyze gaps and recommend / implement improvements AWS Cloud Infrastructure : Familiarity with S3, EC2, Lambda and using these services in ML workflows AWS data : Redshift, Glue Containerization and Orchestration : Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS) To research, modify, and apply data science and data analytics prototypes. To create and construct methods and plans for machine learning. Employing test findings to do statistical analysis and improve models. To search the internet for training datasets that are readily available. ML systems and models should be trained and retrained as necessary. To improve and broaden current ML frameworks and libraries. To create machine learning applications in accordance with client or customer needs. To investigate, test, and put into practice appropriate ML tools and algorithms. To evaluate the application cases and problem-solving potential of ML algorithms and rank them according to success likelihood. To better comprehend data through exploration and visualization, as well as to spot discrepancies in data distribution that might affect a models effectiveness when used in practical situations. Advanced math and statistics knowledge, particularly in the areas of calculus, linear algebra, and Bayesian statistics. Advanced degree in math, computer science, statistics or a related field. A masters degree in artificial intelligence, deep learning, or a related discipline. Strong teamwork, problem-solving, and analytical skills. Abilities in software engineering. Knowledge of data science. Languages for coding and programming, such as Python, Java, C++, C, R, and JavaScript. Practical understanding of ML frameworks. Practical familiarity with ML libraries and packages. Recognize software architecture, data modelling, and data structures. Understanding of computer architecture
Description :
- Core Responsibilities :
The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency
Skills :
Required experience :
Skills : Aws, Aws Cloud, Amazon Redshift,Eks
Roles and Responsibilities of a Machine Learning Engineer
Skills of an ML Engineer :
A person who wants to work as a machine learning engineer needs to possess the following skills and credentials :
(ref : hirist.tech)
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Machine Learning Engineer • Pune
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