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Olbrix — AI / ML Consultancy

AI/ML systems, shipped to production.

Generative AI, MLOps, and data science — designed, built, and shipped for teams that need results, not demos.

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  • 0%avg accuracy lift
  • 0.0×faster inference
  • 0+systems in production
  • 0.0%pipeline uptime
01 — services

What I build

  • Generative AI & LLMs

    From prototype to production-grade GenAI systems — with the evaluation harnesses and guardrails that make them trustworthy at scale.

    • RAG pipeline architecture (hybrid retrieval, reranking, citation validation)
    • Prompt engineering & system-prompt design
    • LLM-as-judge evaluation harnesses with CI integration
    • Hallucination detection & mitigation strategies
    • Fine-tuning scoping, dataset curation, and LoRA/PEFT training
    • Cost optimisation: semantic caching, model routing, prompt budgeting

  • ML Engineering & MLOps

    Reproducible, monitored, auditable ML systems — so your models keep working after the project ends.

    • Training pipeline design (Metaflow, Ray, Prefect)
    • Model registry & versioning (MLflow, W&B)
    • CI/CD for ML: automated retraining, champion/challenger promotion
    • Drift detection & automated alerting
    • Containerised inference (Docker, Kubernetes, AWS EKS/GCP GKE)
    • HIPAA / SOC 2 compliant ML architecture

  • Data Science & Predictive Analytics

    Rigorous statistical and ML modelling that turns raw data into decisions — with the infrastructure to keep predictions fresh.

    • Demand forecasting and time-series modelling
    • Feature store design and feature engineering pipelines
    • Churn prediction, LTV modelling, and segmentation
    • A/B test design, causal inference, and experiment analysis
    • Dashboard and reporting infrastructure (dbt, Snowflake, Tableau)
    • Model interpretability and stakeholder-facing explainability
02 — work

Selected case studies

03 — process

How I work

  1. Discover

    A focused technical audit of your data, infrastructure, and problem definition. We identify the highest-leverage ML intervention and scope a prototype that validates the core assumption in days, not months.

  2. Prototype

    A working system — not a slide deck. End-to-end data pipeline, baseline model, and an eval harness that gives you a real quality signal before committing to production engineering.

  3. Ship

    Production-grade implementation: reproducible training pipelines, monitored inference, CI/CD gates, and documentation your team can own. We hand over something maintainable, not a black box.

  4. Scale

    Ongoing model health, drift monitoring, and performance optimisation. Retrain triggers, cost guardrails, and quarterly technical reviews — so the system keeps improving after launch.

04 — about

Built on production scar tissue

I'm a machine learning engineer and data scientist who has spent the last several years building AI systems that actually stay working in production — not just demos that look good on launch day. I've shipped RAG pipelines for regulated fintech, distributed forecasting platforms for logistics operators, and HIPAA-compliant MLOps infrastructure for clinical AI teams.

My background spans the full ML stack: statistical modelling and feature engineering at the bottom, distributed training and inference infrastructure in the middle, and the evaluation harnesses and monitoring systems that sit on top and keep everything honest. I'm most useful to teams who have a working prototype and need to close the gap to production — or who have something in production that they're afraid to touch.

I work as a boutique consultancy: small engagements, direct access, no account managers. Every engagement gets my full attention and results I'm prepared to put my name on.

currently exploring: Structured outputs for multi-step agentic reasoning, long-context retrieval evaluation benchmarks, and the operational patterns that make multi-agent systems debuggable in production.

PythonPyTorchTransformersLangChainLlamaIndexRayMetaflowPrefectMLflowWeights & BiasesFastAPIPostgreSQLpgvectorRedisDockerKubernetesAWSGCPSnowflakedbtTerraformGitHub Actions
05 — insights

Thinking in public

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06 — contact

Have a model that needs to ship?

The fastest way to scope a project is a 30-minute call. No slides — just a direct technical conversation about your problem.

Book a call

Prefer to write? hello@olbrix.ai