Farhad Davaripour
Ai Engineer @Capital Power
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WORK HISTORY
Ai Engineer @Capital Power
Calgary, AB, CA
Building a power unit outage forecasting classifier using a Batch Temporal Fusion Transformer (BatchTFT) architecture, enhanced with mechanistic interpretability to improve reliability and predictive accuracy- Classified unstructured RFI data using embeddings and K-means, optimized with PCA. Generated reproducible cluster labels using top TF-IDF terms and validated results with a unique LLM-based strategy. This approach improved clustering accuracy, reproducibility, and reliability- Developed an end-to-end power generation market forecasting pipeline in Databricks using Monte Carlo simulation and genetic algorithm, reducing runtime from ~10 hours in the legacy VBA to under 30 minutes through python implementation, horizontal scaling and Spark parallelism- Developed a deep research agent to discover data within a specific domain, validate it, and convert it into a structured format.
EDUCATION
Sharif University of Technology
Master of Engineering (M.Eng.)
Memorial University of Newfoundland
Doctor of Philosophy (PhD)
Imam Khomeini International University
Bachelor of Science (B.Sc.)
SKILLS
ABOUT FARHAD DAVARIPOUR
Tech Lead, Software Engineer, and Machine Learning Engineer with ~10 years of multidisciplinary experience across engineering, software development, analytics, data science, and GenAI.I design and ship production software, ML, and LLM applications (RAG, agentic workflows/ReAct, function calling), and build scalable data/ML pipelines on Databricks/Spark across Azure and AWS.What sets me apart is my strength in system design, distributed performance optimization, and rigorous validation to deliver reliable solutions under real enterprise constraints (security, latency, and cost).Keywords: Software Engineer (Machine Learning), Machine Learning Engineer, Applied Scientist, LLMs, RAG, embeddings, vector search, agents, LLM evaluation, MLOps/LLMOps, Python, PySpark, Spark, SQL, Databricks, Snowflake, Azure OpenAI, AWS, model deployment, distributed systems.
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