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

Job description

**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 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**.