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Jobs(26,659)/ML Engineer Role(126)/smart-working-solutions (7) - Smart Working Solutions - Senior ML Engineer (Remote, Contract) [HR216]
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smart-working-solutions - Smart Working Solutions - Senior ML Engineer (Remote, Contract) [HR216]

Remote - India / Chandigarh / New Delhi / Hyderabad / Pune / Indore / Bangalore3w ago
RemoteSeniorAPACArtificial IntelligenceML EngineerSmart Contract EngineerMLOpsDockerKubernetesObservableAirflow

Requirements

• Proven track record operating at the intersection of Machine Learning, MLOps and Platform/Backend Engineering. • Deep understanding of evaluation metrics for generative AI, LLMs and/or speech models, including Precision, Recall, F1, WER/CER, groundedness and hallucination rates. • Strong hands-on experience with containerisation using Docker and Kubernetes, alongside modern orchestration frameworks. • Experience building observable ML systems with logging, monitoring and strict cost-attribution tracking. • Familiarity with data provenance, compliance standards and building fail-safe mechanisms for sensitive data and AI outputs.

Responsibilities

• Refactor and upgrade existing ML models, including NLP, generative AI and transcription models, into standardised, production-ready modular contracts. • Engineer resilient ML workflows using DAG-based orchestration tools such as Argo Workflows and Airflow. • Ensure robust retry logic, error handling and repeatable execution across ML workflows. • Define, automate and maintain strict ML evaluation pipelines using golden datasets. • Ensure new models meet baseline quality and performance thresholds before release. • Design and implement tracking mechanisms to capture precise model, prompt and input data provenance for full auditability and reproducibility. • Build infrastructure for shadow testing and A/B testing on live traffic. • Implement reliable fallback and kill-switch mechanisms to support safe deployment. • Engineer structured feedback pipelines that capture human reviews and corrections to continuously enrich and refine training datasets. • Integrate third-party AI APIs and manage adapter interfaces. • Implement granular telemetry to track compute costs, token usage and latency.

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