Senior ML Engineer / Data Scientist

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

Personal Profile

• Strong ownership mindset
• Analytical and data-driven thinking
• Ability to work independently in ambiguous environments
• Continuous improvement approach
• Attention to quality and operational excellence
• Effective collaboration and communication skills

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