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HireAI/MLEngineers

Engineers who ship LLM and ML features that survive production — not just demos.

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  • Pre-vetted senior engineers
  • LLM apps in production
  • MLOps end-to-end
  • Onboarding within 5 business days

About this role

AI / ML engineers we place.

AI features only matter if they ship and stay reliable. Our AI engineers build retrieval-augmented LLM apps with proper evaluation, classical ML pipelines with reproducible training, and integrations into existing products that pass user testing. They know what fails in production because they have shipped through it.

Skills & expertise

What our ai / ml engineers cover.

  • 01

    LLM applications

    RAG with hybrid search, agent frameworks, tool use, function calling, structured output. Eval harnesses with regression tracking.

  • 02

    Classical ML

    Tabular forecasting, anomaly detection, recommendation systems. Feature engineering, cross-validation, calibration.

  • 03

    Computer vision

    OCR, object detection, defect detection, video analytics. On-device deployment to edge MCUs and SBCs.

  • 04

    Speech & audio

    Whisper integration, wake-word detection, speaker diarization, audio classification on edge.

  • 05

    MLOps

    Training pipelines, model registries, drift monitoring, canary rollouts, A/B testing of model versions.

  • 06

    Responsible AI

    Bias audits, hallucination detection, prompt-injection guards, compliance with sector regulation (HIPAA, GDPR, EU AI Act).

Hiring models

Three ways to engage a ai / ml engineer.

  • Most popular

    Dedicated developer

    A senior engineer working 40 hours a week as part of your team. Daily standups, your tools, your timezone overlap.

    • Full-time on your engagement
    • Direct Slack / standup integration
    • Monthly billing on time-and-materials
    • Replacement guarantee in first two weeks
    Hire dedicated

  • Part-time / hourly

    Specialist depth on demand for short-cycle work — audits, spikes, code review, migration plans. Minimum 20 hours per month.

    • 20–80 hours per month
    • Hourly logging with weekly summary
    • Same vetting as dedicated engineers
    • Stop or scale up at any month boundary
    Hire part-time

  • Fixed-scope project

    Bounded engagement with a defined deliverable, milestone billing, and a clear exit. Best when scope is stable and timeline matters.

    • Fixed-scope SOW with milestones
    • Milestone-based billing
    • Includes hypercare on launch
    • Ideal for MVPs and audits
    Scope a project

What sets us apart

Why hire from Diglogic.

  • Production track record

    Our AI engineers have shipped LLM features that customers actually use — not slide deck demos.

  • Eval before claim

    Every model we ship has a regression eval suite. You can tell when accuracy drifts before customers can.

  • MLOps thinking baked in

    Models behave like services: versioned, observable, rollbackable. No notebook in production.

  • Privacy first

    Customer data does not train models. Inference logs are minimised. Compliance fits in from day one.

  • Time-zone fit

    4+ hours daily overlap with your team.

  • Replacement guarantee

    Two-week trial period.

Tech stack

Tools they reach for.

  • Python
  • PyTorch
  • Hugging Face
  • LangChain
  • OpenAI
  • Anthropic
  • Pinecone
  • Weaviate
  • MLflow
  • Ray
  • TensorFlow Lite
  • NVIDIA Triton

Hiring process

Brief to first commit.

  1. 01

    Tell us the role

    Share the spec — required experience, stack, time-zone overlap, deadline. Ten minutes on a call or a written brief.

  2. 02

    CVs in 48 hours

    We send 2–4 pre-vetted CVs from engineers who actually fit, not just match keywords.

  3. 03

    You interview

    You run your own technical interview with the candidate. We facilitate; we do not edit.

  4. 04

    Onboard within 5 days

    Once you pick, the engineer is in your tools in five business days. Two-week trial period included.

FAQ

What teams ask before they hire.

Can your engineers fine-tune models or just call APIs?
Both. Fine-tuning is the right call when you have labelled data and a stable problem. Frontier-API + RAG is right for almost everything else. We will tell you which fits.
Do you build on-device AI as well as cloud?
Yes. TinyML on MCUs, edge inference on Jetson/Raspberry Pi/i.MX, mobile model deployment. Hand-in-glove with our IoT engineers.
How do you handle prompt injection and safety?
Threat model on day one. Output filters, input sanitisation, scope limits, structured output schemas. We document residual risks; we do not pretend they are zero.
Do you sign Business Associate Agreements for healthcare?
Yes. We have shipped HIPAA-aware ML pipelines with BAA in place.

Other roles

More engineers we place.

Ready to ship

Hire a ai / ml engineer.

Send the role brief. We come back within 48 hours with 2–4 senior CVs that actually fit.

Request a CV Book a meeting