Machine Learning Engineer

Description : - Core Responsibilities : The MLE will design, build, test, and deploy scalable machine learning systems, optimizing model accuracy and efficiency- Model 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.Skills : - 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.Required experience : - 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)Skills : Aws, Aws Cloud, Amazon Redshift,EksRoles and Responsibilities of a Machine Learning Engineer- 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.Skills of an ML Engineer : A person who wants to work as a machine learning engineer needs to possess the following skills and credentials : - 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 (ref:hirist.tech)