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Senior AI/ML Engineer

We are looking for a Senior AI/ML Engineer to build and operate production-grade AI systems that convert complex, unstructured technical content into high-quality structured data and power fast, accurate search.

 

This is not a research-only role. You will own the full lifecycle:

Architecture → Experimentation → Deployment → Observability → Continuous Improvement

If you enjoy shipping real systems used in production — not just notebooks — this role is for you.

 

 What You Will Own

1. End-to-End ML Architecture

  • Design hybrid systems combining:
    • Computer Vision (detection, segmentation)
    • Layout understanding
    • OCR pipelines
    • NLP processing
  • Transform unstructured documents into structured, searchable representations
  • Design hybrid retrieval strategies (dense + sparse ranking)

 

2. Production ML Systems

  • Ship containerized ML services to AWS
  • Build scalable inference microservices
  • Optimize pipelines for:
    • Latency
    • Throughput
    • Cost
    • Reliability
  • Define and maintain service-level objectives (SLOs)

 

3. Search & Relevance Engineering

  • Implement embedding pipelines
  • Design hybrid search ranking logic
  • Work with vector databases and similarity search
  • Improve recall and precision via measurable experiments

 

4. MLOps & Operational Excellence

  • Implement reproducible ML workflows:
    • Model & dataset versioning
    • CI/CD for ML
    • Automated validation
    • Rollout / rollback strategies
  • Build observability using:
    • OpenTelemetry
    • Prometheus
    • Grafana
  • Set up dashboards and alerts for:
    • Model drift
    • System degradation
    • Latency spikes

 

5. Evaluation & Quality Control

  • Design “Gold Standard” datasets and labeling guidelines
  • Define measurable quality metrics & acceptance thresholds
  • Conduct systematic error analysis
  • Maintain prioritized improvement backlogs

 

Required Experience

🔹 Background

  • 7+ years backend engineering (Python)
  • 5+ years hands-on ML engineering in production
  • Proven experience deploying ML systems into real-world environments

🔹 Computer Vision, Object detection, Image segmentation, OCR pipeline design, Layout-aware document processing, NLP, Information extraction, Intent recognition, Annotation parsing, Structured data transformation, ML & Engineering Stack, PyTorch (preferred), TensorFlow, scikit-learn, Strong software engineering 

 

fundamentals:

  • Clean architecture
  • Testing
  • Code review
  • Maintainable system design

🔹 Search & Retrieval

  • Vector databases
  • OpenSearch / Elasticsearch
  • Dense + sparse hybrid search strategies

🔹 Cloud & Infrastructure

  • AWS
  • Docker
  • Kubernetes
  • CI/CD pipelines
  • Microservices architecture

🔹 Production Operations

  • Observability and alerting systems
  • High-compliance / secure environments
  • Data isolation requirements

 

Employment Details

  • Contract Type: B2B
  • Compensation: Gross, negotiable
  • Public Holidays: 10 per year
    (Vacation & sick leave unpaid)
  • Fully Remote

 

If you are passionate about building real-world AI systems at scale, we’d love to hear from you.

Required languages

English C1 - Advanced
Published 2 March
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