Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK)
Summary
Senior Machine Learning Engineer (remote contract) who builds and modernises an AI platform: productionising NLP and generative AI models, building ML pipelines and evaluation/observability infrastructure, and handling MLOps and backend integration work, primarily in Python.
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
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Refactor, modernise and productionise existing ML models and Applied AI capabilities, including NLP and generative AI solutions.
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Build new ML components and re-engineer existing models into standardised, production-ready modular components.
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Develop production ML applications and supporting services primarily using Python.
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Build and maintain reliable ML pipelines covering model integration, evaluation, deployment and operation.
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Engineer resilient ML workflows with appropriate retry logic, error handling and repeatable execution.
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Design and automate model evaluation pipelines using golden datasets and appropriate quality and performance thresholds.
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Evaluate different types of models using metrics appropriate to their outputs, including generative AI, classification and other ML use cases.
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Implement appropriate guardrails and evaluation mechanisms to assess grounding, hallucinations and quality of generative AI outputs.
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Apply Applied AI techniques, including RAG, where appropriate to the ML capabilities being developed.
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Design mechanisms for model, prompt and input-data provenance to support auditability and reproducibility.
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Build infrastructure supporting shadow testing, A/B testing, fallback strategies and kill switches for safe ML deployment.
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Support the labelling, curation and ongoing development of golden datasets used for model evaluation.
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Build structured human-in-the-loop feedback pipelines to capture reviews and corrections and improve ML datasets.
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Integrate third-party AI APIs and build appropriate adapter/API interfaces.
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Implement observability and telemetry covering model behaviour, errors, compute costs, token usage and latency.
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Contribute backend engineering capability required to integrate ML components reliably into the wider application.
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Support both batch and real-time ML workloads as the platform develops.
Requirements
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6+ years of professional AI/Machine Learning experience, with genuine production experience.
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5+ years of professional MLOps experience.
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At least 2+ years of real Applied AI experience, working with AI/ML capabilities beyond experimentation or personal projects.
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Strong professional Python experience; Python is the core programming language for this role.
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Experience with Docker and Kubernetes.
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Proven experience productionising and deploying AI/ML applications and models.
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Strong understanding of both Applied AI/ML and MLOps, rather than experience limited solely to model research or experimentation.
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Strong hands-on experience with model evaluation and defining appropriate quality/performance criteria for production ML systems.
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Experience working with generative AI/LLMs and understanding evaluation considerations such as grounding and hallucination.
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Hands-on understanding of RAG and other Applied AI techniques.
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Experience building and operating ML pipelines and production ML architectures.
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Experience designing reliable ML workflows with appropriate error handling, retry mechanisms and repeatable execution.
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Experience working with golden datasets and using them for model evaluation and quality gating.
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Experience building observable ML systems using appropriate logging, monitoring and telemetry.
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Understanding of model/data provenance, auditability and reproducibility.
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Experience implementing safe production deployment practices for ML systems, including appropriate testing, fallback or fail-safe mechanisms.
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Sufficient backend engineering experience to build APIs, integrations and production-ready services around ML capabilities.
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Experience solving real production ML problems, including reliability, deployment, integration, evaluation or performance challenges.
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Familiarity with governance, compliance and safeguards relating to sensitive data and AI-generated outputs.
Nice to Have
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Experience with FastAPI for building Python-based ML APIs.
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Exposure to Argo Workflows or similar DAG-based orchestration frameworks.
- Experience working with one or more major cloud platforms: AWS, Azure or GCP.
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Multi-cloud or cloud-agnostic application experience.
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Experience or understanding of TypeScript and/or Go.
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Production experience with speech-to-text or transcription models.
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Experience working with real-time ML applications.
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Experience with traditional NLP models, transformer-based models, encoders and decoders.
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Experience integrating external models/providers such as OpenAI or Claude.
Skills
As published by lever · 17 questions
Basics
In which location did you find the job?, Resume/CV, Full name, Email, Phone, Current location, Current company, LinkedIn URL, Twitter URL, GitHub URL, Portfolio URL
Pick from a list (17)
- Please respond truthfully. How many years of professional experience do you have working in AI/Machine Learning, including production ML systems?
- Please respond truthfully. How many years of professional experience do you have working with MLOps?
- Please respond truthfully. How many years of professional experience do you have working specifically with Applied AI?
- Please respond truthfully. How would you describe your professional experience using Python to build production AI/ML applications and supporting services?
- Please respond truthfully. How would you describe your experience productionising and deploying AI/ML models or applications?
- Please respond truthfully. How would you describe your experience designing and implementing model evaluation for production ML, generative AI or LLM systems?
- Please respond truthfully. How would you describe your professional experience with generative AI/LLMs, including evaluating grounding, hallucinations and output quality?
- Please respond truthfully. How would you describe your hands-on experience implementing RAG in professional AI/ML applications?
- Please respond truthfully. How would you describe your experience building and operating production ML pipelines and orchestrated ML workflows?
- Please respond truthfully. How would you describe your experience building observable and reliable production ML systems, including logging, monitoring, telemetry and fail-safe mechanisms?
- Please respond truthfully. What is your level of spoken and written English?
- Are you comfortable working the fixed shift hours of 8:00 AM – 5:30 PM UK time, Monday to Friday, ensuring at least 6 hours of overlap with UK business hours?
- We are looking for candidates who can start within the next 30 days. ⚠️ Providing false or misleading information will result in disqualification. Candidates progressing to the next stage will be required to upload proof of their notice period or last working day before meeting the client. When is your earliest realistic start date?
- What is your current salary in USD PER YEAR? We will cross-check references later in the process. optional
- What is your minimum expected salary in USD PER YEAR? optional
- Are you open to negotiation?
- Do you acknowledge that submitting more than one application for the same role, or providing any false or misleading information, will automatically and permanently disqualify you from the recruitment process?