Middle Machine Learning Engineer
About this role
Engineers who thrive on turning unstructured data into intelligent systems will find a natural home building document processing solutions at Exadel. The engineering team collaborates with a major enterprise client in the vehicle lifecycle sector to construct advanced machine learning pipelines that process massive documents on AWS. Daily work involves designing document classification models, scaling data pipelines for large PDFs, and applying generative AI techniques like RAG and multi-modal models to complex extraction challenges. This role exists to support growing enterprise demands for automated document intelligence, bridging the gap between raw data and production-ready applications. Candidates who enjoy taking features from initial research all the way through deployment and monitoring will succeed in this environment.
What you will do
- Design and build complete end-to-end document intelligence pipelines hosted on Amazon Web Services.
- Develop, train, and optimize machine learning models specifically for document classification, segmentation, and field extraction.
- Construct scalable data processing systems capable of handling large PDF documents containing up to two thousand pages.
- Partner closely with subject matter experts to gather, refine, and translate business requirements into technical extraction specifications.
- Take full ownership of software features moving from early research through production deployment and ongoing monitoring.
- Create robust evaluation frameworks and quantitative quality metrics to measure and improve extraction accuracy.
What you need
- Demonstrated professional coding experience using Python along with core libraries like Pandas, ScikitLearn, Tensorflow or PyTorch, PyStats, and Pydantic.
- Hands-on background utilizing Amazon Web Services tooling designed for machine learning engineering and deployment such as SageMaker, Lambda, CloudFormation, and Step Functions.
- Advanced technical knowledge regarding relational databases, SQL queries, and enterprise data modeling principles.
- Practical experience implementing Generative AI solutions for document intelligence including prompt engineering, retrieval-augmented generation, multi-modal models, and production APIs like AWS Bedrock.
- Solid understanding of experiment design methodologies including power analysis and hypothesis testing.
- Strong written and verbal communication skills tailored for a remote and largely asynchronous team environment.
- Proven capability to independently own software features from concept and proposal to final execution and production.
Nice to have
- Experience with document processing tools such as AWS Textract, Azure Document Intelligence, or similar OCR and layout detection systems
- Experience with PDF and Image processing libraries including PyMuPDF, OpenCV, and Pillow
- Background in machine learning and data science covering algorithm selection, feature engineering, model training, and hyperparameter tuning
- Familiarity working with Amazon Web Services big data technologies like Redshift, S3, EMR, and Glue
- Upper-intermediate or higher English proficiency
Benefits
- International projects
- In-office, hybrid, or remote flexibility
- Medical healthcare
- Recognition program
- Ongoing learning and reimbursement
- Well-being program
Skills and tech stack
Questions candidates ask
What is the remote work policy for this position?
The position offers in-office, hybrid, or remote flexibility. It operates within a remote and largely asynchronous work environment.
What tools and technologies will I be using?
The tech stack includes Python, AWS tools like SageMaker and Lambda, SQL, and generative AI technologies such as RAG and AWS Bedrock.
When is compensation discussed during the hiring process?
Compensation details are shared with candidates at the early stage of the recruitment process.
What kind of team and projects will I work with?
You will collaborate with subject matter experts to build document intelligence pipelines for a leading provider of vehicle lifecycle solutions.