Careers / Machine Learning Engineer

Machine Learning Engineer

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.

Key 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.

Requirements

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

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

What you get

Flexible working hours;
Competitive compensation commensurate with your experience and skills;
Modern technologies, popular on the market;
Not boring English classes;
Interesting customers and projects;
Learning and development opportunities along with AWS certification program;
An excellent team with a friendly atmosphere.

Level

Middle
Senior

Time

Full Time
Part Time

Location

Remote

Team

Global Team

Technologies

MLOps
OCR
Python
Document processing

Why join Matoffo

Purpose-Driven Innovation

Create AWS-powered, cloud-native solutions that tackle real-world challenges in healthcare, finance, retail, education, and beyond. See the impact of your work – on people and businesses – every single sprint.

Impact Through Collaboration

Blend your domain know-how with Matoffo’s deep Cloud expertise to deliver rapid, high-value outcomes today while laying a resilient foundation for tomorrow’s breakthroughs.

Grow with Certified Excellence

Join a passionate crew of certified professionals who champion continuous learning, creativity, and technical mastery – so your career accelerates as fast as the cloud itself.

Shape Your Cloud Future

Ready to amplify your tech capabilities? Step into a supportive, fun culture where your ideas drive innovation and your success is our mission. Build the cloud-powered future with Matoffo.