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.

Job Summary:

We are looking for a motivated and enthusiastic Trainee DevOps Engineer to join our team. This is an excellent opportunity for fresh graduates or individuals looking to start their career in DevOps. You will gain hands-on experience with cloud platforms, automation tools, CI/CD pipelines, and infrastructure management while working closely with experienced DevOps professionals.

Key Responsibilities:

• Assist in designing, implementing, and maintaining CI/CD pipelines.
• Work with cloud platforms like AWS to deploy and manage applications.
• Learn and use automation tools such as Terraform, Ansible, and Docker.
• Support monitoring and logging systems to ensure application performance and availability.
• Collaborate with development and operations teams to enhance deployment efficiency.
• Troubleshoot system issues and participate in incident resolution.
• Document processes, best practices, and solutions.

Requirements:

• Bachelor's degree in Computer Science, IT, or a related field (or equivalent experience).
• Basic understanding of Linux/Windows systems and networking.
• Familiarity with scripting languages (e.g. Python).
• Knowledge of cloud computing concepts (AWS is a plus).
• Interest in automation, CI/CD, and infrastructure as code (IaC).
• Strong problem-solving skills and eagerness to learn.
• Good communication and teamwork abilities.
   

Nice to Have:

• Exposure to Git and version control systems.
• Basic knowledge of containerization (Docker, Kubernetes).
• Understanding of Agile and DevOps methodologies.

Вимоги до володіння мовами

Англійська

B1 – Середній

A platform that connects to data from every possible source and lets you interact with it through multiple interfaces - knowledge bases, APIs, hardware, and robotics. We are looking for a specialist to join our team:

General:

• Experience: 3+ years of commercial development.

Core Stack:

• Frontend: Next.js, React, TypeScript, Tailwind CSS.
• Backend: Node.js, Express, PostgreSQL.
• Infrastructure: AWS (повний цикл: від роботи з SDK до деплою/сервісів).

Specialized Knowledge:

• AI/LLM Integration: Досвід роботи з моделями, обробка промптів та стрімінг відповідей.
• High-Performance Widgets: Розробка легких JS-скриптів для вбудовування на сторонні сайти (Native TS/CSS, esbuild).
• Observability: Робота з системами логування та моніторингу помилок (Sentry).
• English: Intermediate+