Anna U.
Senior Machine Learning Engineer
Experience
May 2024 - Present
1 year 7 monthsNew York, United States
Senior Machine Learning Engineer
Klarity Labs
- Designed and deployed LLM and Transformer-based NLP models using Hugging Face, BERT, and RoBERTa.
- Developed cloud-native ML APIs using FastAPI, containerized with Docker, and deployed to AWS Lambda.
- Implemented real-time inference monitoring with Grafana and Prometheus to track model health.
- Experimented with reinforcement learning algorithms (PPO, SAC) for optimizing real-time decision-making in sensor-driven environments.
- Conducted applied research on multi-agent RL strategies to optimize distributed control across sensorized environments.
- Built automated model retraining pipelines using MLflow and GitHub Actions with data versioning.
- Worked with software engineers to integrate model outputs into backend systems via RESTful services.
- Collaborated with stakeholders to define AI solution requirements and deployed tailored models on cloud platforms, aligning with business objectives.
- Partnered with healthcare clients to design AI-powered patient engagement and contact center solutions, ensuring compliance with HIPAA and healthcare-specific KPIs.
- Supported model governance by maintaining artifacts, lineage, and reproducibility standards.
- Led technical documentation and onboarding for new MLOps team members.
- Explored multi-sensor data (camera + LiDAR) for 3D object detection and scene segmentation to improve perception pipelines.
- Prototyped auto-labeling workflows and synthetic data augmentation techniques to expand training datasets for NLP and CV models.
Feb 2021 - Apr 2024
3 years 3 monthsSan Francisco, United States
Machine Learning Engineer
Tech Innovate Solutions
- Developed recommendation systems and classification models using PyTorch and Scikit-learn.
- Managed infrastructure for distributed model training across AWS and GCP with autoscaling enabled.
- Built and maintained ETL pipelines with Airflow and SQL, transforming multi-source datasets for ML model training and reporting.
- Designed modular codebase for experimentation and testing with MLflow tracking.
- Collaborated with frontend and backend teams to integrate ML services into customer-facing applications.
- Conducted weekly code reviews and mentored junior ML engineers on model deployment best practices.
- Applied advanced feature engineering and time-series forecasting (ARIMA, LSTM) to predict demand patterns and optimize operational workflows.
Jan 2019 - Dec 2020
2 yearsRedmond, United States
Machine Learning Engineer – AI Platform
Microsoft
- Created MLOps pipelines using Azure ML, Databricks, and Azure Kubernetes Service (AKS).
- Worked on responsible AI implementation using SHAP, LIME, and built model explainability dashboards.
- Developed streaming anomaly detection and control optimization pipelines leveraging Azure ML and reinforcement learning methods, including safe RL approaches, to improve robustness in safety-critical systems.
- Developed internal AutoML utilities and managed GPU resource scheduling across distributed jobs.
- Led development of model cataloging framework for enterprise-wide discoverability and reuse.
- Built streaming models for anomaly detection integrated with Azure Event Hubs and Kafka pipelines.
- Leveraged statistical analysis and predictive modeling to inform decision-making and enhance operational efficiency in large-scale production environments.
- Integrated Azure Cognitive Services (vision, speech, and text APIs) into enterprise applications to deliver intelligent automation and enhance customer experiences.
- Collaborated with healthcare compliance and IT teams to deliver HIPAA-compliant AI workflows and supported healthcare clients in deploying AI solutions for clinical and operational use cases.
- Participated in security compliance checks, model audits, and contributed to Microsoft AI knowledge base.
- Collaborated with cross-functional teams on multi-view perception models, integrating data from vision and sensor streams for anomaly detection and semantic segmentation.
Mar 2013 - Dec 2018
5 years 10 monthsBoston, United States
AI Developer
NextGen Analytics
- Built computer vision models using YOLO, CNNs, and OpenCV for defect detection in industrial systems.
- Designed OCR-based document parsing modules for digitizing scanned forms.
- Packaged AI models as microservices with Flask APIs and deployed on Docker containers.
- Applied ML to industrial control systems, leveraging sensor data (camera, LiDAR, radar) to build adaptive models for anomaly detection and optimization.
- Collaborated with product owners to integrate AI into B2B platforms for analytics and reporting.
- Automated training and testing workflows with shell scripts and version-controlled notebooks.
- Worked closely with QA teams to establish baseline accuracy and validate model predictions.
- Developed early-stage autonomous perception prototypes leveraging CNNs and sensor fusion approaches for object detection.
Summary
Senior Machine Learning Engineer & Applied AI Researcher with 10+ years of experience in reinforcement learning, deep learning, and intelligent control systems. Specialized in reinforcement learning, deep learning, and cloud-native MLOps (AWS, GCP, Azure). Proven expertise in designing ML pipelines, deploying predictive models for structured and time-series data, and integrating AI/ML solutions to optimize business operations and decision-making.
Skilled in PyTorch, TensorFlow, Kubernetes, and experiment tracking (Ray, MLflow, W&B). Strong background in computer vision, anomaly detection, and integrating AI into industrial workflows to drive efficiency and adaptive control.
Languages
English
NativeEducation
National University of Sciences and Technology
Bachelor in Computer Sciences · Computer Science · Islamabad, Pakistan
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