Matoffo

Responsibilities

Model Fine-Tuning and Deployment:

Fine-tune pre-trained models (e.g., BERT, GPT) for specific tasks and deploy them using Amazon SageMaker and Bedrock.

RAG Workflows:

Establish Retrieval-Augmented Generation (RAG) workflows that leverage knowledge bases built on Kendra or OpenSearch. This includes integrating various data sources, such as corporate documents, inspection checklists, and real-time external data feeds.

MLOps Integration:

The project includes a comprehensive MLOps framework to manage the end-to-end lifecycle of machine learning models. This includes continuous integration and delivery (CI/CD) pipelines for model training, versioning, deployment, and monitoring. Automated workflows ensure that models are kept up-to-date with the latest data and are optimized for performance in production environments.

Scalable and Customizable Solutions:

Ensure that both the template and ingestion pipelines are scalable, allowing for adjustments to meet specific customer needs and environments. This involves setting up RAG workflows, knowledge bases using Kendra/OpenSearch, and seamless integration with customer data sources.

End-to-End Workflow Automation:

Automate the end-to-end process from user input to response generation, ensuring that the solution leverages AWS services like Bedrock Agents, CloudWatch, and QuickSight for real-time monitoring and analytics.

Advanced Monitoring and Analytics:

Integrated with AWS CloudWatch, QuickSight, and other monitoring tools, the accelerator provides real-time insights into performance metrics, user interactions, and system health. This allows for continuous optimization of service delivery and rapid identification of any issues.

Model Monitoring and Maintenance:

Implement model monitoring to track performance metrics and trigger retraining as necessary.

Collaboration:

Work closely with data engineers and DevOps engineers to ensure seamless integration of models into the production pipeline.

Documentation:

Document model development processes, deployment procedures, and monitoring setups for knowledge sharing and future reference.

Must-Have Skills

Machine Learning: Strong experience with machine learning frameworks such as TensorFlow, PyTorch, or Hugging Face Transformers.

MLOps Tools: Proficiency with Amazon SageMaker for model training, deployment, and monitoring.

Document processing: Experience with document processing for Word, PDF, images.

OCR: Experience with OCR tools like Tesseract / AWS Textract (preferred)

Programming: Proficiency in Python, including libraries such as Pandas, NumPy, and Scikit-Learn.

Model Deployment: Experience with deploying and managing machine learning models in production environments.

Version Control: Familiarity with version control systems like Git.

Automation: Experience with automating ML workflows using tools like AWS Step Functions or Apache Airflow.

Agile Methodologies: Experience working in Agile environments using tools like Jira and Confluence.

Nice-to-Have Skills

LLM: Experience with LLM / GenAI models, LLM Services (Bedrock or OpenAI), LLM abstraction like (Dify, Langchain, FlowiseAI), agent frameworks, rag.

Deep Learning: Experience with deep learning models and techniques.

Data Engineering: Basic understanding of data pipelines and ETL processes.

Containerization: Experience with Docker and Kubernetes (EKS).

Serverless Architectures: Experience with AWS Lambda and Step Functions.

Rule engine frameworks: Like Drools or similar

If you are a motivated individual with a passion for ML and a desire to contribute to a dynamic team environment, we encourage you to apply for this exciting opportunity. Join us in shaping the future of infrastructure and driving innovation in software delivery processes.

We are looking for a detail-oriented and proactive Manual QA Engineer to join our team on a part-time basis for an internal company project. We highly value flexibility, transparent communication, and a strong focus on quality. In this role, you will be responsible for validating the quality of the product while closely collaborating with developers and project managers.

Responsibilities:

•  Conduct manual testing  functional, regression, UI/UX, and exploratory testing.
• Prepare, maintain, and execute test documentation such as test plans and test cases.
• Detect, document, and track defects, providing clear reproduction steps along with screenshots or logs.
• Collaborate with the development team to clarify requirements and ensure timely resolution of issues.
• Regularly report on testing progress and overall product quality.

Requirements:

• 3+ years of hands-on experience in manual software testing.
• Good understanding of the Software Development Life Cycle (SDLC) and Software Testing Life Cycle (STLC).
• Experience working with browser DevTools (Console, Network) for debugging purposes.
• Basic experience with API testing tools such as Postman or Swagger.
• Fundamental knowledge of SQL.

Soft Skills:

• Strong analytical thinking and attention to detail.
• Ability to work independently and effectively manage time in a part-time setup.
• Proactivity and the capacity to take responsibility for one’s actions and work efficiency.
• English level: Intermediate (B1+) or higher, sufficient for clear written communication and documentation.
   

Working Conditions:

• Format: Part-time.
• Location: Fully remote.
• Schedule: Flexible working hours with mandatory participation in sync meetings (typically in the morning or afternoon).

We are looking for a meticulous and proactive Manual QA Engineer to join our team on a part-time basis. Project related to the development of your own product. We value flexibility, clear communication, and a sharp eye for detail. You will be responsible for ensuring the quality of diverse projects, working closely with developers and project managers. 
 

Key Responsibilities:

• Manual Testing: Perform functional, regression, UI/UX, and exploratory testing on web and mobile applications.
• Documentation: Create, maintain, and execute comprehensive test plans and test cases.
• Bug Reporting: Identify, document, and track defects providing clear reproduction steps and screenshots/logs.
• Collaboration: Work closely with the development team to clarify requirements and ensure timely bug fixes.
•  Reporting: Provide regular updates on testing progress and product quality status.

Requirements:

• Experience: 2+ years of experience in manual software testing.
• Solid understanding of the Software Development Life Cycle (SDLC) and STLC.
• Experience with DevTools (Network, Console) for debugging.
• Familiarity with API testing (Postman or Swagger).

• Basic knowledge of SQL queries.

   

 Soft Skills:

• Strong analytical skills and attention to detail.
• Ability to work independently and manage time effectively in a part-time setup.
• English Level: Intermediate (B1+) or higher. You should be able to write clear documentation.

• Proactivity and the capacity to take responsibility for one’s actions and work efficiency.

   

  Working Conditions:

• Format: Part-time (approx. 20 hours per week).
• Location: Fully remote.

Schedule: Flexible hours, but with mandatory attendance at sync meetings (usually in the mornings/afternoons).

We are seeking a detail-oriented Data Labeler to review and annotate medical documents. The primary responsibilities include categorizing documents by type (e.g., lab results, imaging reports, clinical notes) and identifying and labeling specific sections within each document (e.g., diagnosis, treatment plan, patient history).

Must Have:

Strong attention to detail and accuracy

Analyst or QA background

Ability to follow guidelines and instructions consistently

Motivation to learn and adapt to new information

Ability to work independently and meet productivity targets

Comfortable reading and processing technical documentation

​Nice to Have:

​Familiarity with medical terminology

Prior experience with medical or healthcare documentation

Background in healthcare, life sciences, or related field

Previous data annotation or labeling experience

​Training will be provided on document types, medical terminology, and labeling protocols.

We are seeking a detail-oriented Data Labeler to review and annotate medical documents. The primary responsibilities include categorizing documents by type (e.g., lab results, imaging reports, clinical notes) and identifying and labeling specific sections within each document (e.g., diagnosis, treatment plan, patient history).

Must Have:

• Strong attention to detail and accuracy
• Analyst or QA background
• Ability to follow guidelines and instructions consistently
• Motivation to learn and adapt to new information
• Ability to work independently and meet productivity targets
• Comfortable reading and processing technical documentation

​Nice to Have:

• ​Familiarity with medical terminology
• Prior experience with medical or healthcare documentation
• Background in healthcare, life sciences, or related field
• Previous data annotation or labeling experience

​Training will be provided on document types, medical terminology, and labeling protocols.