• Audit, secure, and optimize our existing cloud infrastructure (AWS) to ensure high availability, fault tolerance, and security for both training and production workloads. • Design and maintain scalable architectures for serving deep learning models (PyTorch/TensorFlow), optimizing for low latency and high throughput in handling complex infrastructure data. • Build and maintain automated pipelines for model testing, validation, deployment, and rollback. • Architect efficient, scalable compute environments for training complex computer vision and time-series models on large datasets. • Implement comprehensive monitoring for model drift, data quality, and system health, ensuring rapid response to performance degradation.

• Design and implement end-to-end ML systems, including data ingestion, feature processing, model training, and model serving • Architect and deploy scalable AI services supporting real-time and batch inference use cases • Build and maintain ML infrastructure across cloud environments (e.g., EC2, EKS, SageMaker, specialized inference hardware) • Develop and evolve MLOps platforms, including training pipelines, deployment workflows, feature stores, and model observability • Implement CI/CD and infrastructure-as-code patterns to automate model lifecycle management • Optimize model training and inference performance for cost, latency, and hardware efficiency • Monitor production ML systems for accuracy, reliability, and operational health • Partner cross-functionally with data engineering, architecture, governance, and security teams to ensure compliant and scalable solutions • Mentor team members on ML engineering, system design, and operational best practices • Contribute to special initiatives that advance AI platform maturity and engineering standards

• Dataset ownership: define specs; audit and curate large-scale audio/text; close corpus gaps and fix sample-level issues. • Quality instrumentation: build automated gates/metrics (e.g., SNR, clipping, VAD, WER, SV/LID, safety) with dashboards; validate against listening tests. • Classifiers and filters: train lightweight models to tag, score, and filter data (VAD, ASR gating, LID, SV/diarization, noise/safety); calibrate to subjective outcomes. • Cleaning and integrity: apply denoise/dereverb/de-clip when beneficial; deduplicate and decontaminate; prevent leakage; maintain lineage and versioned releases. • Data selection: optimize mixtures via sampling, weighting, curriculum, and active learning; mine hard negatives and long-tail cases. • Tooling and pipelines: ship reproducible ETL and validation; integrate quality gates into training/eval; add monitoring and alerts. • Human-in-the-loop and compliance: run MTurk/vendor annotation with strong QC; ensure consent/licensing/policy compliance; collaborate across teams and document datasets.

• Work with business and operational teams to understand the problem, the operational and system constraints, the available data, and decision points. • Define how opportunities fit within the long-term evolution of the systems. • Based on the problem and the available data, determine how we can measure success and monitor system outcomes/dynamics. • Define and drive the evolution of the solution, identifying points of high leverage, and phases/evolution of the system. • Evolve plans and direction as we iteratively gather feedback and learn about solution effectiveness. • Collaborate with cross-functional leaders to identify opportunities, communicate and evaluate solutions, and gather feedback. • Mentor of data scientists and analysts, fostering a collaborative and high-performing environment. • Provide technical guidance, set standards, and ensure the team uses appropriate tools and methodologies. • Stay current with emerging trends in data science, AI, and big data technologies to drive innovation and maintain a competitive advantage.