Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK)

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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.
• Experience with **Docker and Kubernetes**.
• 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 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**.