Parham Dehghani
Ai Annotator @TELUS Digital AI Data Solutions
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WORK HISTORY
Ai Annotator @TELUS Digital AI Data Solutions
San Francisco, CA, US
Evaluated AI-generated content for relevance, accuracy, and cultural appropriateness to improve LLM fine-tuning quality.• Assessed content using structured evaluation guidelines, providing granular feedback on tone, intent, and local linguistic context.• Contributed to RLHF pipelines by delivering high-quality, consistent data annotations.• Maintained top-tier accuracy and throughput under tight deadlines in a production AI evaluation setting.
EDUCATION
McGill University
Inter-university program, Particle Physics PhD
Concordia University
Particle Physics PhD
Udacity
Data Scientist Nanodegree Program, Artificial Intelligence
Oxford Mathematics
Summer School, ML Representation Learning and Generative AI
ABOUT PARHAM DEHGHANI
Data scientist with a background in computational particle physics, specializing in model training/evaluation, deployment, and production systems with proven experience building containerized ML pipelines (Docker, Kubernetes, Airflow) and deploying models on AWS SageMaker and GCP Vertex AI. Developed automated CI/CD workflows (GitHub Actions, Jenkins) and monitoring systems (Prometheus, Grafana) for different use cases. Strong background in Slurm cluster computation (CPU & GPU), parallel processing, statistical modeling, rigorous testing, and optimization across the ML lifecycle.Here is the recap of my skills in action:• MLOps & Deployment: Docker, Kubernetes, CI/CD (GitHub Actions, Jenkins), AWS SageMaker Pipelines, GCP Vertex AI Pipelines, Airflow, MLflow, FastAPI (model serving), TorchServe, Monitoring & Observability (Prometheus, Grafana)• Programming & Libraries: Python (NumPy, Pandas, scikit-learn, TensorFlow, PyTorch), SQL, Bash• Data Engineering & Orchestration: ETL, Data Pipelines, PySpark, Dask, Feature Stores, Data Versioning (DVC)• Cloud Platforms: AWS (SageMaker, S3, Lambda, EC2, RDS), GCP (Vertex AI, BigQuery ML, AutoML)• Core AI & Machine Learning: Supervised & Unsupervised Learning, Deep Learning, Generative AI (LLMs, RAG), LLM Fine-tuning (SFT, RLHF), Model Optimization & Quantization, LangChain (Agents/ReAct)• Analytics & Experimentation: Feature Engineering, A/B Testing, Statistical Modeling, Exploratory Data Analysis (EDA)• Visualization Tools: Matplotlib, Seaborn, Tableau• Collaboration & Version Control: Git, Cross-functional Collaboration, Agile Workflows• Interpersonal: Problem-solving, Attention to Detail, Communication, Time Management• Languages: English & FrenchPlease check my GitHub for the latest projects: https://github.com/parhamdehghani
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