Senior ML Engineer (Remote, Full-Time) [HR216]
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Job Description
Structured overview of role & requirementsAbout This Role
Upgrade and standardize ML models (NLP, generative AI, transcription) into production-ready modular contracts.
Build and maintain resilient ML workflows with DAG orchestration (e.g., Argo Workflows, Airflow) including retry logic and error handling.
Engineer evaluation pipelines, tracking mechanisms for model data provenance, live testing infrastructure, fallback systems, and feedback loops for continuous data enrichment.
Minimum Requirements
Experience at the intersection of Machine Learning, MLOps, and Platform/Backend Engineering (work experience: Not explicitly mentioned in the JD).
Deep knowledge of evaluation metrics for generative AI, LLMs, speech models (Precision, Recall, F1, WER/CER, groundedness, hallucination rates).
Hands-on experience with Docker, Kubernetes, and modern orchestration frameworks.
Experience with observable ML systems including logging, monitoring, cost tracking, and data provenance with compliance and fail-safe mechanisms.
Ideal Candidate Profile
Operationally strong in engineering production ML workflows with emphasis on reliability, observability, and auditability.
Experienced in managing complex AI model lifecycle including evaluation, deployment safeguards, and continuous feedback integration.
Technical familiarity with containerization and orchestration in high-scale ML environments supporting generative AI and speech technologies.