Job Description
<p><p><b>Description : </b><br/><br/>- Core Responsibilities : <br/><br/> The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency<br/><br/>- Model Development : Algorithms and architectures span traditional statistical methods to deep learning along with employing LLMs in modern frameworks.<br/><br/>- Data Preparation : Prepare, cleanse, and transform data for model training and evaluation.<br/><br/>- Algorithm Implementation : Implement and optimize machine learning algorithms and statistical models.<br/><br/>- System Integration : Integrate models into existing systems and workflows.<br/><br/>- Model Deployment : Deploy models to production environments and monitor performance.<br/><br/>- Collaboration : Work closely with data scientists, software engineers, and other stakeholders.<br/><br/>- Continuous Improvement : Identify areas for improvement in model performance and systems.<br/><br/><b>Skills : </b><br/><br/>- Programming and Software Engineering : Knowledge of software engineering best practices (version control, testing, CI/CD).<br/><br/>- 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<br/><br/>- Model Deployment and Monitoring : MLOps Experience in deploying ML models to production environments.<br/><br/>- Knowledge of model monitoring and performance evaluation.<br/><br/><b>Required experience : </b><br/><br/>- 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<br/><br/>- AWS Cloud Infrastructure : Familiarity with S3, EC2, Lambda and using these services in ML workflows<br/><br/>- AWS data : Redshift, Glue<br/><br/>- Containerization and Orchestration : Understanding of Docker and Kubernetes, and their implementation within AWS (EKS, ECS)<br/><br/><b>Skills : </b>Aws, Aws Cloud, Amazon Redshift,Eks<br/><br/><b>Roles and Responsibilities of a Machine Learning Engineer</b><br/><br/>- To research, modify, and apply data science and data analytics prototypes.<br/><br/>- To create and construct methods and plans for machine learning.<br/><br/>- Employing test findings to do statistical analysis and improve models.<br/><br/>- To search the internet for training datasets that are readily available.<br/><br/>- ML systems and models should be trained and retrained as necessary.<br/><br/>- To improve and broaden current ML frameworks and libraries.<br/><br/>- To create machine learning applications in accordance with client or customer needs.<br/><br/>- To investigate, test, and put into practice appropriate ML tools and algorithms.<br/><br/>- To evaluate the application cases and problem-solving potential of ML algorithms and rank them according to success likelihood.<br/><br/>- 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.<br/><br/><b>Skills of an ML Engineer : </b><br/><br/>A person who wants to work as a machine learning engineer needs to possess the following skills and credentials : <br/><br/>- Advanced math and statistics knowledge, particularly in the areas of calculus, linear algebra, and Bayesian statistics.<br/><br/>- Advanced degree in math, computer science, statistics or a related field.<br/><br/>- A masters degree in artificial intelligence, deep learning, or a related discipline.<br/><br/>- Strong teamwork, problem-solving, and analytical skills.<br/><br/>- Abilities in software engineering.<br/><br/>- Knowledge of data science.<br/><br/>- Languages for coding and programming, such as Python, Java, C++, C, R, and JavaScript.<br/><br/>- Practical understanding of ML frameworks.<br/><br/>- Practical familiarity with ML libraries and packages.<br/><br/>- Recognize software architecture, data modelling, and data structures.<br/><br/>- Understanding of computer architecture</p><br/></p> (ref:hirist.tech)