This is a fully remote position, open to applicants in India.

📋 Description

• Revise and enhance current ML models, including NLP, generative AI, and transcription models, into standardized, production-ready modular contracts.

• Develop resilient ML workflows utilizing DAG-based orchestration tools such as Argo Workflows and Airflow.

• Ensure strong retry logic, error handling, and repeatable execution across ML workflows.

• Define, automate, and sustain stringent ML evaluation pipelines using golden datasets.

• Confirm that new models meet baseline quality and performance criteria prior to release.

• Design and implement tracking systems to capture accurate model, prompt, and input data provenance for comprehensive auditability and reproducibility.

• Construct infrastructure for shadow testing and A/B testing on live traffic.

• Implement dependable fallback and kill-switch mechanisms to facilitate safe deployment.

• Engineer structured feedback pipelines that gather human reviews and corrections to continuously enhance and refine training datasets.

• Integrate external AI APIs and manage adapter interfaces.

• Establish detailed telemetry to monitor compute costs, token usage, and latency.

⛳️ Requirements

• Demonstrated experience operating at the intersection of Machine Learning, MLOps, and Platform/Backend Engineering.

• In-depth knowledge of evaluation metrics for generative AI, LLMs, and/or speech models, including Precision, Recall, F1, WER/CER, groundedness, and hallucination rates.

• Strong practical experience with containerization using Docker and Kubernetes, alongside contemporary orchestration frameworks.

• Experience in building observable ML systems with logging, monitoring, and strict cost-attribution tracking.

• Familiarity with data provenance, compliance standards, and developing fail-safe mechanisms for sensitive data and AI outputs.