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,Eks

Roles 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