Stefan is available for hire
Hire StefanStefan Mićić
Verified Expert in Engineering
Python Data Engineer and Developer
Novi Sad, Vojvodina, Serbia
Toptal member since July 20, 2022
Bio
Stefan is an experienced machine learning and machine learning operations (MLOps) engineer with hands-on experience in big data systems. His almost a decade of expertise is supplemented by a master's degree in artificial intelligence. Stefan has worked on problems such as object detection, classification, sentiment analysis, named-entity recognition (NER), and recommendation systems. He is always looking forward to being involved in end-to-end machine learning projects.
Portfolio
RhythmScience Inc.
Machine Learning, Python, Keras, PyTorch, Deep Learning, Scikit-learn...
PlusPower
Python 3, Amazon SageMaker, Amazon Web Services (AWS), Docker, Bitbucket...
Cumulus Technologies LLC
Artificial Intelligence (AI), Machine Learning, Python...
Experience
Availability
Part-time
Preferred Environment
PyCharm, Python 3, Python, GitHub, Amazon S3 (AWS S3), JSON, Distributed Systems
The most amazing...
...end-to-end machine learning solution I've created optimized the cost of the machine learning pipelines numerous times with state-of-the-art results.
Work Experience
Machine Learning Engineer
2023 - PRESENT
RhythmScience Inc.
- Deidentified the database and various types of files (HL7, XML, and PDF) by HIPAA standards and dockerized and automated the whole pipeline.
- Developed ML algorithms to generate text and classify PDF reports.
- Designed, implemented, and deployed the solution.
Technologies: Machine Learning, Python, Keras, PyTorch, Deep Learning, Scikit-learn, Natural Language Processing (NLP), Generative Pre-trained Transformers (GPT), Data Integration, Intelligent Content Processing (ICP)
Senior MLOps Engineer
2023 - 2024
PlusPower
- Developed big ML pipelines using Sagemaker including preprocessing, training, evaluation and deployment.
- Developed pipeline that was able to generate airflow pipelines based on configs and automated deployment of DAGs.
- Increased test coverage from 15% to 80% and added integration tests so that we can test sagemaker pipelines locally.
Technologies: Python 3, Amazon SageMaker, Amazon Web Services (AWS), Docker, Bitbucket, DocumentDB, Grafana, Datadog, Terragrunt, Apache Airflow, Pytest, Terraform, Infrastructure
AI Lead (via Toptal)
2023 - 2024
Cumulus Technologies LLC
- Created the whole CI/CD pipeline on AWS. Everything from data ingestion, processing, and model training to model deployment was automated.
- Designed and led the implementation of the whole ML pipeline using various AWS services such as Lambda, Polly, and SageMaker.
- Utilized AWS for development to meet high security requirements (AWS Cloud9, AWS CodeCommit, and AWS CodePipeline).
- Used prompt engineering to force the model to find answers from different sources.
Technologies: Artificial Intelligence (AI), Machine Learning, Python, Amazon Web Services (AWS), Amazon SageMaker, Machine Learning Operations (MLOps), Hyperledger Fabric, Google Cloud Platform (GCP), SQL, PostgreSQL, Database Migration, Large Language Models (LLMs), Models, Unit Testing, English, Generative Artificial Intelligence (GenAI), Language Models, Stock Trading, Algorithmic Trading, Finance, Financial Software, Trading Systems, OpenAI, Prompt Engineering, Retrieval-augmented Generation (RAG), OpenAI GPT-3 API, OpenAI GPT-4 API, APIs, Speech Recognition, System Architecture, Infrastructure, Google Cloud, ChatGPT
MLOps Engineer
2023 - 2023
NewsCorp
- Performed the deployment of different LLM and Stable Diffusion models.
- Worked on latency and cost optimizations of LLMs. Successfully reduced latency by five times using different deployment techniques.
- Took responsibility for the complete deployment process of the whole ML part and documentation maintenance.
- Used prompt engineering to make LLM execute the NER tasks.
- Used RAG approach in couple of chatbots.
Technologies: Amazon EC2, GitHub, Docker, Deep Learning, Models, Unit Testing, English, Query Optimization, Language Models, Retrieval-augmented Generation (RAG), APIs, Infrastructure
MLOps Engineer
2022 - 2023
PepsiCo Global - DPS
- Implemented an end-to-end machine learning pipeline using PySpark.
- Set up CI/CD workflows with unit and integration tests using GitHub Actions.
- Developed Spark and scikit-learn/Pandas ETL jobs to process large data volumes (150 TB).
Technologies: Machine Learning Operations (MLOps), APIs, Machine Learning, Python, Databricks, Big Data, Spark, Scikit-learn, Pandas, CI/CD Pipelines, REST APIs, ETL, Models, Unit Testing, Data Processing, English, Query Optimization, MLflow, Data Analytics, Infrastructure
Tech Lead Data Engineer
2022 - 2023
Motius
- Led a small team in implementing an ELT pipeline to get data from a GraphQL database and put it into Azure SQL. Everything was Dockerized and pushed to Azure Image Registry.
- Implemented KPI calculations using PySpark, which was communicating with Snowflake. Defined table schema for Snowflake and created migration scripts.
- Followed the Scrum methodology, including daily scrums, retro, and planning, and used Jira.
- Led a small team in implementing ETL Spark jobs with Apache Airflow as an orchestrator, AWS as infra and Snowflake as a data warehouse.
Technologies: Spark, Apache Spark, PySpark, Snowflake, Python, Python 3, Amazon Web Services (AWS), Databases, Distributed Systems, Azure SQL, Azure, AWS Glue, Apache Airflow, Software Architecture, Data Pipelines, Data Analysis, CI/CD Pipelines, Database Migration, Data Engineering, ETL, Unit Testing, Data Processing, English, Query Optimization, Data Analytics, Data Integration, ELT, DataOps
MLOps Engineer
2021 - 2022
Lifebit
- Carried out deep learning model optimizations using quantization, ONNX Runtime, and pruning, among others.
- Monitored model performance, including memory, latency, and CPU usage.
- Used Valohai to automate the CI/CD process and GitHub Actions to automate some parts of the MLOps lifecycle.
- Created automated experiment tracking using Amazon CloudWatch, Valohai, Python, GitHub Actions, and Kubernetes.
Technologies: Amazon EC2, Valohai, Keras, TensorFlow, Python 3, Lens Studio, Kubernetes, Codeship, GitHub, Open Neural Network Exchange (ONNX), Visual Studio Code (VS Code), Optimization, Neural Networks, NumPy, Monitoring, Amazon S3 (AWS S3), Cloud, Scikit-learn, Amazon Web Services (AWS), AI Design, Deep Neural Networks, Software Engineering, Pytest, JSON, Source Code Review, Code Review, Task Analysis, Databases, Data Science, CI/CD Pipelines, DevOps, REST APIs, Models, Unit Testing, English, Language Models, APIs, Amazon SageMaker, Terraform, Celery, Infrastructure, Ray.io
Machine Learning Engineer
2020 - 2021
HTEC Group
- Optimized a machine learning compiler already on a trained network without re-training using Open Neural Network Exchange (ONNX) and implemented custom operators using PyTorch and C++.
- Worked on an Android machine learning solution and mentored a less experienced developer to train and prepare an object detector and classifier to run smoothly on an Android device.
- Enhanced a project that aimed to upscale images to be as perfect as possible toward 4K resolution.
- Involved in SDP of ship routing problem. Implemented an algorithm from scratch that will guide the ships. Fuel consumption and ETA were used for calculations.
- Worked on open source ONNX Runtime in order to add support for the MIGraphX library.
Technologies: Python 3, Python, Docker, Computer Vision, PyTorch, Artificial Intelligence (AI), Machine Learning, Team Leadership, Machine Learning Operations (MLOps), GitHub, Convolutional Neural Networks (CNN), Open Neural Network Exchange (ONNX), Visual Studio Code (VS Code), Neural Networks, NumPy, Cloud, Pandas, Scikit-learn, Computer Vision Algorithms, AI Design, Deep Neural Networks, Software Engineering, Pytest, JSON, Technical Hiring, Source Code Review, Code Review, Task Analysis, Interviewing, Databases, Data Science, REST APIs, Models, Unit Testing, English, Language Models, Research, APIs
Machine Learning Engineer
2019 - 2020
SmartCat
- Contributed to complete MLOps lifecycles using MLflow for model versioning, LakeFS for data versioning, AWS S3 for data storage, and TensorFlow serving in Docker.
- Functioned as a data engineer using Apache Spark for ETL jobs with Prefect and Apache Airflow for scheduling.
- Trained several different architectures for object detection and classification.
Technologies: Python 3, Scala, Python, Docker, SQL, Computer Vision, MongoDB, Artificial Intelligence (AI), Machine Learning, Data Engineering, Machine Learning Operations (MLOps), GitHub, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNN), ETL, Visual Studio Code (VS Code), Neural Networks, NumPy, Amazon S3 (AWS S3), Big Data, Image Processing, Cloud, Pandas, Scikit-learn, Object Detection, Computer Vision Algorithms, Object Tracking, Apache Spark, Amazon Web Services (AWS), AI Design, Deep Neural Networks, Software Engineering, Pytest, ETL Tools, JSON, Jupyter Notebook, Source Code Review, Code Review, Task Analysis, PySpark, Databases, Data Science, Distributed Systems, Data Pipelines, REST APIs, Models, Unit Testing, Data Processing, English, MLflow, APIs, Amazon SageMaker, Prefect
Machine Learning Engineer
2016 - 2019
Freelance
- Scraped product information from various websites, then analyzed and prepared the scraped data for web shops using natural language processing—long short-term memory (LSTM), Word2Vec, and transformers—and added NER since the data was in Serbian.
- Used Amazon SageMaker to automate the machine learning pipeline—data preprocessing, model training, and deployment. Executed automated retraining and deployment of the model, completing the machine learning process before the client updated new data.
- Worked on big data projects using Apache Spark, Kafka, Hadoop, and MongoDB.
- Worked as a data engineer using Spark to create optimized ETL pipelines. Translated the client's needs into SQL.
Technologies: Python 3, Spark, Amazon SageMaker, Python, Docker, Computer Vision, MongoDB, Artificial Intelligence (AI), Machine Learning, Data Engineering, Kubernetes, Machine Learning Operations (MLOps), GitHub, Amazon EC2, Recurrent Neural Networks (RNNs), Convolutional Neural Networks (CNN), Open Neural Network Exchange (ONNX), Recommendation Systems, Natural Language Understanding (NLU), Generative Pre-trained Transformers (GPT), Natural Language Processing (NLP), Visual Studio Code (VS Code), Time Series, Data Modeling, Data Mining, Neural Networks, NumPy, Amazon S3 (AWS S3), Big Data, Apache Kafka, Hugging Face, Transformers, Cloud, Pandas, Scikit-learn, Object Detection, Computer Vision Algorithms, Apache Spark, Amazon Web Services (AWS), AI Design, Web Development, Deep Neural Networks, Software Engineering, Pytest, JSON, Jupyter Notebook, Source Code Review, Code Review, Task Analysis, PySpark, Databases, Data Science, Distributed Systems, Project Management, CI/CD Pipelines, Google Cloud Platform (GCP), DevOps, REST APIs, Models, Unit Testing, English, MLflow, APIs
Experience
Automated End-to-end (E2E) Computer Vision Solution
Created a system that performed several things in real-time, including:
• Detecting objects in the room
• Classifying person poses
• Automated re-training (active learning)
• Model and data versioning
• Dockerized pipeline
Using those models and predictions, we created a post-processing pipeline for creating reports or key performance indicators (KPIs) for clients.
• Detecting objects in the room
• Classifying person poses
• Automated re-training (active learning)
• Model and data versioning
• Dockerized pipeline
Using those models and predictions, we created a post-processing pipeline for creating reports or key performance indicators (KPIs) for clients.
Android COVID-19 Test Classification
The goal was to create a COVID-19 test classification model. We had a small dataset and had to build the best model in the shortest possible time (two weeks).
I led a team of two people on this project. We used MobileNet due to size, and all business-relevant metrics were great. We used many optimization techniques to deploy the model to Android, such as quantization, pruning, and knowledge distillation.
I led a team of two people on this project. We used MobileNet due to size, and all business-relevant metrics were great. We used many optimization techniques to deploy the model to Android, such as quantization, pruning, and knowledge distillation.
MLOps Engineer
Participated in a project where my job was to optimize the whole machine learning system using quantization, pruning, ONNX, and more. I achieved the same accuracy with five times reduced latency, two times reduced model size, and four times reduced cost. I also changed the type of underlying EC2 instances to get more of our system.
Image Super Resolution
The goal was to improve the model for upscaling and super-resolution by researching and developing approaches from SOTA research papers. There were a lot of different custom loss function, layers, metrics, and even custom back propagations.
ETL Jobs
• Created batch ETL jobs for calculating KPIs.
• Optimized solution to reduce cost and calculation time.
• Scheduled jobs via Airflow and Prefect.
The tech stack was: Spark, Scala, AWS S3, Kafka, Apache Airflow, and Prefect.
• Optimized solution to reduce cost and calculation time.
• Scheduled jobs via Airflow and Prefect.
The tech stack was: Spark, Scala, AWS S3, Kafka, Apache Airflow, and Prefect.
NLP Articles Processing
The goal of this project was to develop two stages of article processing:
1. Find all relevant tags (events, locations, names, etc.) in the article.
2. Find pairs of tags that are somehow related.
Hugging Face transformers were mainly used to tackle this problem (BERT-based models). Overall metrics were above 95%.
1. Find all relevant tags (events, locations, names, etc.) in the article.
2. Find pairs of tags that are somehow related.
Hugging Face transformers were mainly used to tackle this problem (BERT-based models). Overall metrics were above 95%.
Data Ingestion
Led a team whose goal was to get data from the GraphQL database and insert it into Azure SQL. Everything was Dockerized and pushed to EKS on every push to the main branch on GitLab. Concurrent threads were used in order to optimize the solution.
Tech Leadership for the DE project
My responsibility was to make all decisions from architectural to the nitty gritty details about the implementation. We used AWS for infra (CloudWatch, Glue, S3) and Airflow to orchestrate Spark jobs. Every result of a Spark job was saved to Snowflake.
Education
2020 - 2021
Master's Degree in Artificial Intelligence
University of Novi Sad - Novi Sad, Serbia
Certifications
JULY 2022 - JULY 2025
AWS Certified Machine Learning - Specialty
Amazon Web Services
Skills
Libraries/APIs
PyTorch, Keras, NumPy, Scikit-learn, REST APIs, TensorFlow, Pandas, PySpark, Terragrunt
Tools
PyCharm, Amazon SageMaker, GitHub, Apache Airflow, Pytest, Open Neural Network Exchange (ONNX), Codeship, Prefect, ChatGPT, AWS Glue, Bitbucket, Grafana, Terraform, Celery, Intelligent Content Processing (ICP), AI Prompts
Languages
Python 3, Python, SQL, Scala, Java, Snowflake, GraphQL, C++
Frameworks
Spark, Apache Spark, Streamlit
Paradigms
ETL, Unit Testing, DevOps
Platforms
Amazon Web Services (AWS), Jupyter Notebook, Visual Studio Code (VS Code), Docker, Kubernetes, Amazon EC2, Valohai, Apache Kafka, Azure, Databricks, Google Cloud Platform (GCP), Hyperledger Fabric, Kubeflow
Storage
Amazon S3 (AWS S3), JSON, Databases, PostgreSQL, NoSQL, MongoDB, Data Pipelines, Database Migration, Data Integration, Azure SQL, Datadog, Google Cloud
Industry Expertise
Trading Systems, Project Management
Other
Deep Learning, Machine Learning, Data Science, Artificial Intelligence (AI), Data Engineering, Computer Vision, Natural Language Processing (NLP), Natural Language Understanding (NLU), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNNs), Machine Learning Operations (MLOps), Neural Networks, AI Design, Deep Neural Networks, Software Engineering, Technical Hiring, Source Code Review, Code Review, Task Analysis, Interviewing, APIs, Generative Pre-trained Transformers (GPT), Large Language Models (LLMs), Models, Data Processing, English, Generative Artificial Intelligence (GenAI), Language Models, MLflow, OpenAI, Recommendation Systems, Lens Studio, Optimization, Team Leadership, Time Series, Data Modeling, Data Mining, Monitoring, Big Data, Image Processing, Transformers, Cloud, Object Detection, Computer Vision Algorithms, Object Tracking, Web Development, Speech Recognition, Voice Recognition, Cloud Services, ETL Tools, Distributed Systems, Data Analysis, CI/CD Pipelines, Query Optimization, Research, Stock Trading, Algorithmic Trading, Finance, Financial Software, Prompt Engineering, Retrieval-augmented Generation (RAG), OpenAI GPT-3 API, OpenAI GPT-4 API, Data Analytics, ELT, System Architecture, Infrastructure, DataOps, Hugging Face, BERT, Back-end, Software Architecture, DocumentDB, Ray.io, AI Translation, FastAPI
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