Senior ML Engineer

Job not on LinkedIn

🔥 0 minutes ago

🇮🇳 India – Remote

⏰ Full Time

🟠 Senior

🤖 Machine Learning Engineer

👻 Ghost score 15%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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Smart Working

WebsiteLinkedInAll Job Openings

51 - 200 employees

💼 Consulting

🏥 Healthcare

📣 Marketing

Consulting • Healthcare • Marketing

Smart Working is a recruitment service specializing in sourcing and providing top-tier software developers from around the world to meet the needs of businesses. With a robust vetting process that includes technical assessments and background checks, Smart Working ensures that clients receive highly skilled developers adept in various programming languages and frameworks. The company focuses on flexible and remote hiring solutions, allowing businesses to efficiently scale their development teams while benefiting from significant cost savings.

📋 Description

• 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

🏖️ Benefits

• Full-time, long-term role • Remote-first work environment • Genuine community focused on growth and well-being • Opportunities for personal and professional growth • Teams that invest in employee success