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Asifa

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Senior AI/ML Engineer
Houston, Texas, United States
Contact Asifa regarding: 
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Full-time jobs
Starting at USD50/hour

Timeline

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Résumé

Jobs verified_user 0% verified
  • Acentra Health
    Senior AI/ML Engineer
    Acentra Health
    Feb 2023 - Jan 2026 (3 years)
    Architected enterprise-wide LLM platform for clinical workflow automation using Azure OpenAI and AWS Bedrock, reducing operational time by 50%. Led design and deployment of large-scale RAG pipelines serving 50K+ clinicians, improving information retrieval accuracy to 94%. Directed migration of legacy ML models to modern cloud-native pipelines (multi-cloud: Azure, AWS, GCP), reducing inference latency by 35%. Mentored a team of 5+ AI/ML engineers, improving team velocity by 30% through code reviews and architecture guidance. Established model observability, governance, and HIPAA-compliant monitoring, improving audit readiness by 70%. Spearheaded cost optimization initiatives across three cloud platforms, saving $300K/year in compute and stor
  • P
    Machine Learning Engineer / AI Engineer (Mid-Level)
    Parkland Health
    Sep 2021 - Feb 2023 (1 year 6 months)
    Designed and deployed end-to-end ML pipelines for patient-risk prediction, reducing model deployment time from 2 weeks → 2 days. Implemented RAG-based clinical question answering system using vector databases, achieving 92% retrieval accuracy. Built production-grade APIs deploying PyTorch/TensorFlow models via FastAPI and Kubernetes, improving system uptime to 99.7%. Integrated CI/CD pipelines for ML workflows on Azure DevOps/GitHub Actions, cutting release cycles by 45%. Implemented monitoring and drift detection using Evidently AI, reducing model performance degradation by 30%. Reduced cloud inference cost by 28% using model quantization + optimized compute selection (Azure/AWS/GCP). Implemented end-to-end MLOps pipelines with Kubernetes,
  • D
    AI Research Engineer / Applied Research Engineer
    Dallas
    Jan 2020 - Aug 2021 (1 year 8 months)
    Developed deep learning models for early disease detection using PyTorch/TensorFlow, achieving 87%+ accuracy in production experiments. Built LLM-based NLP prototypes for clinical summarization using GPT-style models, reducing manual documentation workload by 40%. Fine-tuned biomedical language models (BioBERT / ClinicalBERT) improving medical text classification F1 by 22%. Designed experiment pipelines handling 1–3 TB datasets, decreasing training cycle time by 30% using distributed training on cloud GPUs. Collaborated with clinical research teams to translate research findings into production-ready machine-learning features, applying HIPAA-compliant data handling and feature-engineering pipelines in AWS, which enabled clinicians to access
  • LawnStarter
    AI/ML Research Assistant / Associate Researcher
    LawnStarter
    Jan 2017 - Dec 2019 (3 years)
    Developed baseline ML models (XGBoost, logistic regression) for churn prediction, improving F1 score from 0.56 → 0.69. Conducted 200+ controlled experiments related to pricing, scheduling, and service provider matching, improving matching efficiency by 18%. Built early prototypes for recommendation systems to match customers with top-performing service providers, increasing job acceptance rate by 15%. Cleaned and preprocessed 1TB+ of consumer behavior data, improving model readiness and reducing pipeline errors by 30%. Implemented NLP models to analyze 50K+ customer reviews, identifying top satisfaction factors and reducing negative review rate by 10%. Reproduced and validated research findings from industry papers using Python and BigQuery
  • A
    AI/ML Research Analyst
    Austin
    Jan 2016 - Dec 2016 (1 year)
    Analyzed 10M+ customer and service provider records to identify demand patterns, improving forecasting accuracy by 28%. Built automated EDA pipelines to evaluate pricing trends across 50+ U.S. cities, reducing manual analysis time by 40%. Conducted statistical modeling on job completion rates, improving prediction accuracy by 22%. Produced weekly analytical reports for operations and product teams, accelerating decision-making by 35%. Improved data quality by creating validation rules that reduced inconsistent entries by 30%. Collaborated with product analysts to explore factors affecting customer churn, helping reduce churn by 12%. Applied Python, SQL, Scikit-learn, and Pandas to perform data analytics, generate actionable reports, and run
Education verified_user 0% verified
  • The University of Texas at Austin
    Bachelor's degree, Computer Science
    The University of Texas at Austin
    Jan 2011 - Jan 2015 (4 years 1 month)
    Austin, TX