Senior ML Engineer (Remote, Contract) [HR216]

Smart Working

IndiaremotePosted 17 days ago

Skill Required

Machine-Learning-EngineerMLOps-EngineerSenior-ML-EngineerAI-EngineerSenior-AI-ML-EngineerSenior-AI-ML-Data-EngineerSenior-ML-EngineeringSenior-Applied-ML-EngineerRemote-Senior-AI-EngineerSenior-AI-ML-DeveloperMachine Learning EngineerMachine LearningGenerative AIObservabilityStatisticsEngineeringKubernetesbuildingAirflowTestNGDockerdesignAPIsNLPandAIContract

Key highlights

Level Required: Senior
Notable requirement: Proven track record operating at the intersection of Machine Learning, MLOps and Platform/Backend Engineering

Role overview

Smart Working connects skilled professionals with global remote-first teams for full-time roles. As a Senior ML Engineer, you will bridge applied machine learning and platform engineering, focusing on translating AI capabilities into reliable, observable system components. You will ensure ML models meet quality, cost, and latency thresholds before production, covering architecture, workflow reliability, evaluation, provenance, testing, feedback, and observability.

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.

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.