Sr. Machine Learning Engineer; Remote HR; PK

Position: Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK)
About Smart Working

At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn't just another remote opportunity - it's about finding where you truly belong, no matter where you are. From day one, you're welcomed into a genuine community that values your growth and well-being.

Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you're empowered to grow personally and professionally.

Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.

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 product ionising, 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 product ionise 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 product ionising 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…