Senior ML Engineer / Data Scientist

Are you passionate about building production-grade AI systems that continuously learn and improve from real-world feedback? We are looking for a Senior ML Engineer / Data Scientist to help develop intelligent recognition and entity-matching solutions for a large-scale media data platform.

In this fully remote role across Europe, you will work with Large Language Models, evaluation frameworks, and cloud-based ML pipelines to improve automation quality and reduce manual processing efforts. You will collaborate closely with Data Engineering teams and Customer stakeholders while owning the ML lifecycle end-to-end.

We at Sigma Software create impactful technology solutions for global customers and provide engineers with opportunities to work on meaningful, high-scale products using modern AI technologies. This role offers significant ownership, challenging engineering tasks, and the ability to influence production AI systems at scale.

Customer

Our Customer operates a large-scale platform focused on processing and structuring advertising and media operational data. The company is actively investing in intelligent automation and machine learning solutions to improve recognition accuracy across multiple station and network layouts while minimizing manual intervention in data processing workflows.

Project

The project focuses on building a self-learning Postlog and Prelog recognition system capable of automatically understanding new layouts, extracting structured data, and improving from production feedback. The solution leverages Large Language Models and modern ML practices to optimize recognition quality, entity matching, and confidence-based automation.

You will contribute to the development of scalable AI-driven workflows designed to achieve high automation accuracy, observability, and operational efficiency in production environments.

Responsibilities

Design and develop a self-learning Postlog and Prelog recognition system using modern ML and LLM techniques

Build and maintain versioned prompts, evaluation datasets, and few-shot exemplars

Apply production-grade LLM practices including schema-constrained extraction, grounding strategies, and low-confidence fallback handling

Improve recognition quality and optimize layout and header mapping performance

Analyze production failures and enhance prompts, retrieval pipelines, and model behavior

Run evaluation pipelines and shadow-mode comparisons against legacy systems and gold datasets

Monitor confidence scores, latency, operational quality, and infrastructure costs

Develop entity-matching systems for Station, Advertiser, and CreativeID master data

Implement confidence scoring, thresholding, and auditability mechanisms

Transform human and machine corrections into labeled signals for continuous model improvement

Monitor prompt and model drift in production environments

Collaborate with Data Engineering teams on ML integration and operationalization

Communicate technical findings and recommendations to engineering teams and Customer stakeholders

Requirements

At least 5 years of experience in Machine Learning, Data Science, or ML Engineering

Proven experience delivering ML models or LLM-powered systems into production

Strong hands-on experience with Large Language Models in real products or pipelines

Deep understanding of prompt engineering, prompt versioning, evaluation methodologies, and grounding strategies

Experience handling low-confidence scenarios and optimizing cost and latency for LLM systems

Strong Python and SQL skills

Solid knowledge of statistics, confidence estimation, sampling, hypothesis testing, and threshold optimization

Experience with classification, ranking, matching, or recommendation-related problems

Understanding of offline evaluation metrics, holdout validation, and production monitoring

Hands-on experience with AWS cloud services including S3, IAM, CloudWatch, and orchestration services

Strong communication and collaboration skills

Upper-Intermediate English level or higher

Will be a plus

LLM-related certifications

Experience with Amazon Bedrock or equivalent enterprise LLM platforms

Production experience with Claude/Sonnet-class models

Experience with Excel or layout extraction systems

Knowledge of confidence calibration, active learning, or weak supervision techniques

Experience with cost-aware LLM operations including caching, routing, and fallback models

Advertising or media domain knowledge

Familiarity with Glue, Airflow, or similar orchestration and data pipeline tools