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AI/ML Engineer with 7+ years of experience designing, developing, and deploying production-grade Artificial Intelligence and Machine Learning solutions across cloud environments. Experienced in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, intelligent automation workflows, and scalable MLOps pipelines. Proficient in Python, TensorFlow, PyTorch, LangChain, OpenAI APIs, Apache Spark, Airflow, and modern cloud platforms including AWS, GCP, and Azure. Skilled in feature engineering, model deployment, vector databases, prompt engineering, CI/CD, and AI system optimization. Passionate about building secure, reliable, and scalable AI solutions that automate engineering workflows and deliver measurable business impact.…AI/ML Engineer with 7+ years of experience designing, developing, and deploying production-grade Artificial Intelligence and Machine Learning solutions across cloud environments. Experienced in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, intelligent automation workflows, and scalable MLOps pipelines. Proficient in Python, TensorFlow, PyTorch, LangChain, OpenAI APIs, Apache Spark, Airflow, and modern cloud platforms including AWS, GCP, and Azure. Skilled in feature engineering, model deployment, vector databases, prompt engineering, CI/CD, and AI system optimization. Passionate about building secure, reliable, and scalable AI solutions that automate engineering workflows and deliver measurable business impact.

Ethan Hassan

AI Engineer, Data Scientist, AI Developer, +2

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AI/ML Engineer with 7+ years of experience designing, developing, and deploying production-grade Artificial Intelligence and Machine Learning solutions across cloud environments. Experienced in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, intelligent automation workflows, and scalable MLOps pipelines. Proficient in Python, TensorFlow, PyTorch, LangChain, OpenAI APIs, Apache Spark, Airflow, and modern cloud platforms including AWS, GCP, and Azure. Skilled in feature engineering, model deployment, vector databases, prompt engineering, CI/CD, and AI system optimization. Passionate about building secure, reliable, and scalable AI solutions that automate engineering workflows and deliver measurable business impact.…AI/ML Engineer with 7+ years of experience designing, developing, and deploying production-grade Artificial Intelligence and Machine Learning solutions across cloud environments. Experienced in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, intelligent automation workflows, and scalable MLOps pipelines. Proficient in Python, TensorFlow, PyTorch, LangChain, OpenAI APIs, Apache Spark, Airflow, and modern cloud platforms including AWS, GCP, and Azure. Skilled in feature engineering, model deployment, vector databases, prompt engineering, CI/CD, and AI system optimization. Passionate about building secure, reliable, and scalable AI solutions that automate engineering workflows and deliver measurable business impact.

Available to hire

AI/ML Engineer with 7+ years of experience designing, developing, and deploying production-grade Artificial Intelligence and Machine Learning solutions across cloud environments. Experienced in building LLM-powered applications, Retrieval-Augmented Generation (RAG) systems, intelligent automation workflows, and scalable MLOps pipelines. Proficient in Python, TensorFlow, PyTorch, LangChain, OpenAI APIs, Apache Spark, Airflow, and modern cloud platforms including AWS, GCP, and Azure. Skilled in feature engineering, model deployment, vector databases, prompt engineering, CI/CD, and AI system optimization. Passionate about building secure, reliable, and scalable AI solutions that automate engineering workflows and deliver measurable business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Senior AIML Engineer at Temporal Technologies

January 1, 2024 - Present

Provided AIML engineering expertise as a consultant through a third-party partner, supporting cloud-based AI/ML platform transformation projects. Led architecture and delivery of enterprise-grade ML feature engineering and model-serving frameworks, enabling reliable, large-scale data processing across hybrid cloud platforms, significantly boosting deployment speed and system reliability. Directed a cross-functional team of 6 engineers to design and deploy a low-latency real-time inference ecosystem with distributed ML serving technologies, delivering near-real-time predictions to millions of platform users. Implemented Python-based proactive model monitoring controls, automated drift-detection mechanisms, and alerting systems — reducing production incidents and increasing stakeholder trust. Optimized distributed training workloads, inference latency, and GPU utilization to cut infrastructure costs. Defined MLOps best practices, coding standards, and reusable model pipeline templates

AIML Engineer at Particle Health

January 1, 2022 - January 1, 2024

Provided AIML engineering expertise as a consultant engaged through a third-party partner, supporting cloud-based AI/ML platform transformation projects. Engineered scalable feature engineering and data pipelines integrating data from 100+ third-party APIs, financial services, and relational data sources to support model training and centralized feature stores. Designed and implemented predictive data models and feature schemas supporting large-volume tax processing, fraud detection, and financial reporting workflows. Collaborated with data science and compliance teams to overhaul model scoring backend structures and optimize inference strategies, reducing batch scoring time from hours to under 10 minutes. Assisted in cloud migration from on-premise infrastructure to AWS, contributing to ML architecture decisions, model pipeline redesign, and testing frameworks. Maintained ML pipeline documentation, runbooks, and model performance monitoring dashboards to ensure operational reliability

AIML Engineer at Planet Scale

January 1, 2020 - January 1, 2022

Developed and maintained end-to-end data and feature pipelines to ingest, transform, and load structured and semi-structured data from multiple internal and external sources to support ML model training. Partnered with data science and product teams to design and deliver model evaluation dashboards and feature data models, reducing manual reporting effort by over 60%. Implemented automated data and model quality frameworks including schema validation, null checks, and anomaly/drift detection — significantly improving pipeline reliability across production ML workloads. Optimized complex SQL queries, Spark jobs, and ML batch training workflows, achieving a 40% reduction in average pipeline execution time and infrastructure overhead. Contributed to the adoption of modern MLOps stack tools including MLflow and Apache Airflow for workflow orchestration and model lifecycle management.