Machine Learning Engineer

Location: Athens, GR, Remote Greece

Remote Type: Hybrid

Time Type: Full time

Job Description

Job Description:

Strategic Imperative: 

The Machine Learning Engineer designs, builds, and productionizes end-to-end AI/ML systems that measurably improve business outcomes. This role owns the technical lifecycle of models from data and features through deployment, monitoring, and continuous improvement. You will ship reliable, well-tested services that move KPIs in areas such as ranking, recommendations, yield optimization, fraud detection, and intelligent routing, while also contributing to emerging AI initiatives including LLMs and agentic systems.

Who We Are!  

Pollfish, a Prodege, LLC company, is an online market research survey platform where data driven brands bring market research in-house for faster and smarter decision making. We have a proprietary network of 250M consumers/year which enables companies to connect with and understand real consumers worldwide in a fast, easy and cost-effective way.

Primary Objectives: 

• End-to-End ML Systems: Build automated, scalable workflows for data ingestion, feature engineering, training, evaluation, and inference.

• Feature Platforms: Develop and maintain high-quality feature stores for batch and real-time use cases.

• Production ML: Package and deploy models as APIs, microservices, or streaming/batch jobs in collaboration with software and data engineering.

• Monitoring and Experimentation: Implement telemetry, data drift detection, performance tracking, and A/B testing to ensure reliability and impact.

• Collaboration: Partner closely with Product, Data, and Engineering to translate business problems into measurable ML solutions.

• Engineering Excellence: Write clean, maintainable, testable code; participate in code reviews; follow strong CI/CD and version control practices.

Qualifications - To perform this job successfully, an individual must be able to perform each job duty satisfactorily. The requirements listed below are representative of the knowledge, skill, and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

Detailed Job Duties:  (typical monthly, weekly, daily tasks which support the primary objectives)

End-to-End ML Systems

• Design, validate, and scale ML models including classification, regression, ranking, recommendations, NLP, and optimization systems.

• Build and maintain end-to-end ML pipelines for training and inference in both batch and streaming environments.

• Run and analyze experiments to improve model performance and business outcomes.

Feature Platforms

• Own feature quality, consistency, and governance across products and services.

• Ensure features are reliable for both offline training and online inference.

• Partner with data engineering on feature pipeline reliability, freshness, and availability.

Production ML

• Deploy models via RESTful APIs, containerized services, or scheduled jobs, with appropriate reliability safeguards.

• Work with software and data engineering to integrate models into production systems.

• Ensure deployments are reproducible, versioned, and compatible with CI/CD practices.

Monitoring and Experimentation

• Instrument production systems with comprehensive monitoring for data drift, latency, prediction quality, and business impact.

• Define success metrics and guardrails for model performance in production.

• Identify degradation, diagnose root causes, and implement fixes in a timely way.

Cross-Functional Collaboration

• Collaborate with stakeholders to define success metrics, communicate progress, and manage risks transparently.

• Translate business requirements into technical ML deliverables.

• Align model design with product roadmaps and engineering constraints.

Engineering Excellence

• Write well-structured, modular, and testable Python code.

• Participate in peer code reviews and uphold team standards.

• Document systems, assumptions, and design decisions clearly.

• Follow version control, testing, and deployment best practices.

Emerging AI Contributions

• Build or integrate retrieval-augmented generation systems using embeddings and vector stores.

• Experiment with prompt design, evaluation frameworks, and fine-tuning where appropriate.

• Apply guardrails, monitoring, and safety controls to LLM-powered features.

Team Development

• Mentor junior engineers when applicable.

• Share learnings, patterns, and reusable components across teams.

• Contribute to shared tooling and best practices.

NOTE: Depending on location or product team, the role may emphasize one of the following without changing level or title:

• AI Platform focus: deeper ownership of shared feature stores, experimentation frameworks, and ML infrastructure.

• Product focus: closer partnership with product teams to optimize ranking, recommendations, or marketplace yield.

• LLM focus: greater time on RAG systems, agentic workflows, and model fine-tuning.

• Data-heavy focus: more work on streaming pipelines and real-time inference.

What does SUCCESS look like?

You consistently ship ML capabilities that are reliable, observable, and impactful. Your work improves measurable KPIs such as accuracy, latency, conversion, revenue lift, or fraud reduction. You balance rigor with pragmatism, document your systems clearly, and help the team move faster through thoughtful experimentation and strong collaboration.

The MUST Haves: (ex: job cannot be done without these skills, education, experience, certifications, licenses) 

• Bachelor’s degree in Computer Science, Engineering, Mathematics, or related field, or equivalent experience.

• 3+ years of hands-on ML engineering experience delivering models to production.

• Strong Python and working SQL; solid software engineering fundamentals including testing, modular design, and version control.

• Experience with classical ML such as tree-based models, linear/logistic models, and model evaluation.

• Working knowledge of a deep learning framework such as PyTorch or TensorFlow.

• Experience building data and feature pipelines for training and inference.

• Experience deploying models via APIs, microservices, or batch/streaming systems.

• Demonstrated ability to improve models through iteration, tuning, or redesign.

• Clear written and verbal communication; ability to partner effectively across teams.

The Nice to Haves: (ex: preferred additional skills, education, experience, certifications, licenses) 

• Master’s degree or PhD in AI, Machine Learning, or a quantitative field.

• Experience with ranking or recommendation systems at scale.

• Experience with distributed computing frameworks such as Spark, Ray, or Flink.

• Cloud experience on AWS, Azure, or GCP plus MLOps tooling such as MLflow or model registries.

• Familiarity with Docker/Kubernetes.

• Exposure to LLM tooling including Hugging Face, embeddings, RAG, or agent frameworks.

• Additional languages such as Java, Scala, or Rust.

Perks & Benefits:

• An attractive salary package

• Part of an innovative Global Tech Company

• Private Health Insurance

• Company Equity

• Weekly Office Events - Catered Lunch and Breakfast

• Stocked Kitchen

• Company Outings & Quarterly Events

• Hybrid Working

• Meal Coupons - Monthly

• LinkedIn Learning & Training Opportunities/Budget

• Mental Health Benefits - Wellness Coach App Subscription

• Great office location in the city center - Parking slots available

• Gym Subscription - UP Fit

• Quarterly Charitable Giving Allowance

• Peer recognition Allowance