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About the Role

We are seeking a Senior ML Engineer with strong experience in Applied AI, Machine Learning and MLOps to build and modernise an AI platform.

The role combines Applied AI, MLOps and backend/platform engineering, with a strong focus on productionising, deploying, evaluating and operating ML/AI systems. You will build new ML capabilities, modernise existing NLP and generative AI systems, and create reliable, observable infrastructure that makes models easier to integrate, evaluate, monitor and deploy.

Responsibilities

• 
  

  Refactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI solutions.



• 
  

  Build new ML components and re-engineer existing models into standardised, production-ready modular components.



• 
  

  Develop production ML applications and supporting services primarily using

  Python

  .



• 
  

  Build and maintain reliable ML pipelines covering model integration, evaluation, deployment and operation.



• 
  

  Engineer resilient ML workflows with appropriate retry logic, error handling and repeatable execution.



• 
  

  Design and automate model evaluation pipelines using golden datasets and appropriate quality and performance thresholds.



• 
  

  Evaluate different types of models using metrics appropriate to their outputs, including generative AI, classification and other ML use cases.



• 
  

  Implement appropriate guardrails and evaluation mechanisms to assess grounding, hallucinations and quality of generative AI outputs.



• 
  

  Apply Applied AI techniques, including

  RAG

  , where appropriate to the ML capabilities being developed.



• 
  

  Design mechanisms for model, prompt and input-data provenance to support auditability and reproducibility.



• 
  

  Build infrastructure supporting shadow testing, A/B testing, fallback strategies and kill switches for safe ML deployment.



• 
  

  Support the labelling, curation and ongoing development of golden datasets used for model evaluation.



• 
  

  Build structured human-in-the-loop feedback pipelines to capture reviews and corrections and improve ML datasets.



• 
  

  Integrate third-party AI APIs and build appropriate adapter/API interfaces.



• 
  

  Implement observability and telemetry covering model behaviour, errors, compute costs, token usage and latency.



• 
  

  Contribute backend engineering capability required to integrate ML components reliably into the wider application.



• 
  

  Support both batch and real-time ML workloads as the platform develops.

Requirements

• 
  

  6+ years of professional AI/Machine Learning experience

  , with genuine production experience.



• 
  

  5+ years of professional MLOps experience.



• 
  

  At least

  2+ years of real Applied AI experience

  , working with AI/ML capabilities beyond experimentation or personal projects.



• 
  

  Strong professional

  Python

  experience; Python is the core programming language for this role.



• 
  

  Proven experience

  productionising and deploying AI/ML applications and models

  .



• 
  

  Strong understanding of both

  Applied AI/ML and MLOps

  , rather than experience limited solely to model research or experimentation.



• 
  

  Strong hands-on experience with

  model evaluation

  and defining appropriate quality/performance criteria for production ML systems.



• 
  

  Experience working with

  generative AI/LLMs

  and understanding evaluation considerations such as grounding and hallucination.



• 
  

  Hands-on understanding of

  RAG and other Applied AI techniques

  .



• 
  

  Experience building and operating

  ML pipelines and production ML architectures

  .



• 
  

  Experience designing reliable ML workflows with appropriate error handling, retry mechanisms and repeatable execution.



• 
  

  Experience working with

  golden datasets

  and using them for model evaluation and quality gating.



• 
  

  Experience building observable ML systems using appropriate logging, monitoring and telemetry.



• 
  

  Understanding of model/data provenance, auditability and reproducibility.



• 
  

  Experience implementing safe production deployment practices for ML systems, including appropriate testing, fallback or fail-safe mechanisms.



• 
  

  Sufficient

  backend engineering experience

  to build APIs, integrations and production-ready services around ML capabilities.



• 
  

  Experience solving real production ML problems, including reliability, deployment, integration, evaluation or performance challenges.



• 
  

  Familiarity with governance, compliance and safeguards relating to sensitive data and AI-generated outputs.

Nice to Have

• 
  

  Experience with

  FastAPI

  for building Python-based ML APIs.



• 
  

  Exposure to

  Argo Workflows

  or similar DAG-based orchestration frameworks.



• 
  

  Experience with

  Docker and Kubernetes

  .



• 
  

  Experience working with one or more major cloud platforms:

  AWS, Azure or GCP

  .



• 
  

  Multi-cloud or cloud-agnostic application experience.



• 
  

  Experience or understanding of

  TypeScript and/or Go

  .



• 
  

  Production experience with

  speech-to-text or transcription models

  .



• 
  

  Experience working with

  real-time ML applications

  .



• 
  

  Experience with traditional NLP models, transformer-based models, encoders and decoders.



• 
  

  Experience integrating external models/providers such as

  OpenAI or Claude

  .

We may use artificial intelligence (AI) tools to support parts of the hiring process, such as reviewing applications, analyzing resumes, or assessing responses and identifying potential inconsistencies or verification signals in application materials based on available information. These tools assist our recruitment team but do not replace human judgment. Final hiring decisions are ultimately made by humans. If you would like more information about how your data is processed, please contact us.