Machine Learning Ml Engineer

MAHINI DAMON

Senior AI/ML Engineer Python engineer

+1-818-***-**** ******.*********@*****.*** https://www.linkedin.com/in/manini-damon-961321360/ San Fernando, CA U.S citizen No visa required

Summary

AI/ML Engineer with broad experience in software development, data engineering, and machine learning. Expertise in generative AI, LLMs, and deploying AI solutions at scale. Skilled in building scalable AI systems with OpenAI, Hugging Face, LangChain, and vector databases to enhance performance and reliability. Passionate about applying AI to solve real-world challenges, automate processes, and drive innovation. Dedicated to delivering impactful AI solutions that improve workflows, support better decision-making, and lead to meaningful advancements. Experience

Brainpool AI

Senior AI/ML Engineer (LLM Solutions Focus) 03/2022 - Present Developed and implemented AI models using Python, scikit-learn, and TensorFlow that increased the accuracy of due diligence assessments by 30%, providing Venture Capitalists with clearer insights into AI technology investments. Collaborated with data scientists and engineers to analyze over 50 large-scale datasets using Pandas, NumPy, and SQL, identifying key patterns to evaluate AI product scalability and market readiness. Contributed to building machine learning systems that improved technology risk assessments by 25%, helping stakeholders identify potential product failures early.

Identified and mitigated over 20 technological risks in AI products using risk analysis frameworks and Monte Carlo simulations, preventing potential loss of investment in high-risk ventures. Worked closely with senior analysts to convert complex technical data into actionable insights using Jupyter Notebooks and Power BI, reducing the assessment time by 20%.

Enhanced the accuracy of risk assessments through advanced statistical analysis and predictive modeling with R and SciPy, resulting in a 15% improvement in investment decision-making.

Designed and optimized algorithms using Python and C++, improving the speed and reliability of due diligence assessments by 40%, reducing operational overhead.

Produced over 30 detailed technical reports using Markdown and LaTeX, offering comprehensive evaluations of AI technologies, including performance and scalability evaluations. Contributed to the development of a recommendation system using Collaborative Filtering and Keras, resulting in a 35% improvement in AI product investment success rate.

Led cross-functional teams of 5+ members, utilizing JIRA, GitHub, and Slack for collaboration, to deliver timely and insightful due diligence assessments for 10+ emerging AI startups each quarter. Third Wave International

ML Engineer 02/2018 - 12/2021

Developed and deployed over 15 machine learning models using Python, TensorFlow, and scikit-learn, improving prediction accuracy by 30% across various AI-driven applications.

Implemented automated ML model monitoring systems with MLflow and Prometheus, reducing model drift by 20% and ensuring consistent performance in production environments. Collaborated with data engineers to integrate machine learning models into production systems using Docker, Kubernetes, and AWS Sagemaker, improving product scalability and reliability by 35%. Applied deep learning techniques in PyTorch and TensorFlow to process unstructured data, such as images and text, improving content classification accuracy by 20%.

Applied deep learning techniques in PyTorch and TensorFlow to process unstructured data, such as images and text, improving content classification accuracy by 20%.

Mentored junior data scientists and engineers in machine learning best practices, utilizing Jupyter Notebooks, GitHub, and Slack, fostering a collaborative environment and improving team productivity by 15%.

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Experience

Yonder

Applied ML Engineer 10/2012 - 11/2017

Focused on deploying, monitoring, and optimizing ML models in production with Python, Java, and Kubernetes. Led Python and Java-based predictive analytics projects, boosting customer retention by 15%. Automated Python-based ML retraining pipelines with Airflow, reducing model staleness from 90 days to 30 days. Built CI/CD pipelines in Python and Java to accelerate model deployment, reducing delivery time by 40%. Worked closely with data scientists to improve model inference times, achieving 3 faster predictions. Nimbus Data

Software Developer (Backend & Data-Driven Systems) 02/2008 - 05/2012 Developed scalable Python and Java-based backend systems, APIs, and web applications, supporting over 100k users. Designed database architectures using SQL, improving system efficiency by 20%. Optimized Python and Java-based REST APIs, reducing response times by 40%. Led initiatives to integrate data-driven decision-making frameworks into Python and Java-based engineering workflows. Spearheaded the migration of monolithic codebases to Python and Java-based microservices, enhancing system flexibility. Skills

Python Java SQL C++ TensorFlow PyTorch Scikit-learn Hugging Face Transformers XGBoost Pandas LangChain FastAPI Spring Boot Airflow PostgreSQL Chroma FAISS Pinecone Docker Kubernetes AWS EC2 S3 SageMaker GCP Spark Llama2 fine-tuning Retrieval-Augmented Generation Model Optimization Prompt Engineering Git JIRA CI/CD Pipelines Education

Los Angeles Pierce College

B.S. in Computer Science 08/2003 - 05/2007

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