smart-working-solutions - Smart Working Solutions - Sr. Machine Learning Engineer (Remote, Contract) [HR216] (PK)
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. • 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.
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. • 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.
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