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Senior ML Engineer (Remote, Contract) [HR216]

Smart Working· Engineering
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Hybrid (India)Part-Time | RemotePosted 2w ago

About the role

Senior ML Engineer with applied AI, machine learning and MLOps focus to build and modernise an AI platform, productionising AI/ML capabilities and integrating them into production systems.

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.
  • Help improve the platform's extensibility, resilience, error tracking and ability to integrate new ML models efficiently.

Qualifications

  • 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.

Skills

Required:PythonDockerKubernetesMLOpsNLP / Generative AIModel evaluationRAGBack-end developmentObservability/Telemetry
Last updated Oct 21, 2026. Interested? Apply on the company site.

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