Location: Remote (must overlap with EST business hours)
Contract Type: B2B
English Level: C1 (Advanced)
Compensation: Gross, TBD
Holidays: 10 public holidays (vacation & sick days unpaid)

About the Role:
We are seeking a talented Machine Learning Engineer with expertise in LLMs, computer vision, and multimodal data processing. You will develop and optimize models to extract insights, generate embeddings, and model relationships across text, images, and structured metadata. Your work will power similarity search, recommendation systems, and other AI-driven applications.

Core Responsibilities:

• Develop, fine-tune, and evaluate LLM and computer vision models for information extraction, embeddings, and relationship modeling across multimodal data.

• Build and refine vector embeddings for text, metadata, and visual representations to support similarity search and recommendation systems.

• Conduct systematic experiments to benchmark models, compare approaches, and improve accuracy, robustness, and retrieval quality.

• Define evaluation strategies for model performance, embedding quality, and search relevance.

• Prepare, clean, and analyze multimodal datasets from drawings, OCR outputs, and structured metadata.

• Perform feature engineering for visual, textual, and structured data used across ML models.

• Validate data consistency, identify issues, and propose improvements to labeling, preprocessing, and dataset structure.

• Collaborate on evaluating similarity search pipelines using Milvus, OpenSearch, or Elasticsearch.

• Analyze retrieval performance, investigate mismatches, and iterate on embeddings and preprocessing logic.

• Prepare trained models for deployment with clear documentation, evaluation reports, and defined inputs/outputs.

• Collaborate with engineering teams on inference pipelines and deployment strategies.

Monitoring & Continuous Improvement:

• Track model performance metrics (accuracy, recall, drift) and investigate error patterns.

• Recommend strategies for new data collection, labeling, or retraining to improve model quality.

• Support continuous improvement cycles driven by feedback and new data.

Collaboration & Documentation:

• Work with AI engineers, data engineers, QA, and business analysts to translate requirements into modeling tasks.

• Document datasets, experiments, and modeling decisions clearly.

• Communicate insights and results effectively to both technical and non-technical stakeholders.

Required Skills & Experience:

Machine Learning:

• Strong understanding of LLMs, computer vision, embedding models, and experimentation workflows.

• Ability to design and rigorously evaluate ML experiments.

Frameworks & Tools:

• PyTorch, TensorFlow, scikit-learn

• Hugging Face Transformers

• OpenCV, TorchVision

Programming & Data:

• Proficiency in Python for data manipulation, experimentation, and modeling

• Experience preparing and analyzing large-scale datasets

• Expertise in feature engineering and data quality assessment

• Familiarity with Milvus, OpenSearch, or Elasticsearch for similarity search

• Experience with AWS, S3, DynamoDB, EKS, Docker, Kubernetes

• Understanding of ML monitoring using OpenTelemetry, Prometheus, and Grafana

Collaboration & Communication:

• Strong ability to communicate modeling insights and data findings clearly

• Comfortable working cross-functionally with engineering, product, and business teams

Why Join Us:

• Work on cutting-edge AI and multimodal ML projects

• Collaborate with a talented, cross-functional team

• Opportunity to impact real-world applications with AI