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I am a results-driven AI/ML Engineer with 4+ years of hands-on experience building and deploying production-scale ML, DL, and Generative AI solutions. I specialize in LLMs, RAG architectures, AI agents, NLP, and MLOps, with deep expertise in Python, PyTorch, TensorFlow, LangChain, LlamaIndex, and Hugging Face. I have a proven track record delivering enterprise AI systems that automate workflows, support large-scale decision-making, and improve efficiency. I excel at building scalable data pipelines, deploying cloud-native AI apps on AWS and Azure, and applying LLMOps, CI/CD, and monitoring frameworks. I thrive in cross-functional teams, translating business requirements into high-impact AI products.…I am a results-driven AI/ML Engineer with 4+ years of hands-on experience building and deploying production-scale ML, DL, and Generative AI solutions. I specialize in LLMs, RAG architectures, AI agents, NLP, and MLOps, with deep expertise in Python, PyTorch, TensorFlow, LangChain, LlamaIndex, and Hugging Face. I have a proven track record delivering enterprise AI systems that automate workflows, support large-scale decision-making, and improve efficiency. I excel at building scalable data pipelines, deploying cloud-native AI apps on AWS and Azure, and applying LLMOps, CI/CD, and monitoring frameworks. I thrive in cross-functional teams, translating business requirements into high-impact AI products.

Sana Pattan

AI Engineer, Data Scientist, Full Stack Developer, +2

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I am a results-driven AI/ML Engineer with 4+ years of hands-on experience building and deploying production-scale ML, DL, and Generative AI solutions. I specialize in LLMs, RAG architectures, AI agents, NLP, and MLOps, with deep expertise in Python, PyTorch, TensorFlow, LangChain, LlamaIndex, and Hugging Face. I have a proven track record delivering enterprise AI systems that automate workflows, support large-scale decision-making, and improve efficiency. I excel at building scalable data pipelines, deploying cloud-native AI apps on AWS and Azure, and applying LLMOps, CI/CD, and monitoring frameworks. I thrive in cross-functional teams, translating business requirements into high-impact AI products.…I am a results-driven AI/ML Engineer with 4+ years of hands-on experience building and deploying production-scale ML, DL, and Generative AI solutions. I specialize in LLMs, RAG architectures, AI agents, NLP, and MLOps, with deep expertise in Python, PyTorch, TensorFlow, LangChain, LlamaIndex, and Hugging Face. I have a proven track record delivering enterprise AI systems that automate workflows, support large-scale decision-making, and improve efficiency. I excel at building scalable data pipelines, deploying cloud-native AI apps on AWS and Azure, and applying LLMOps, CI/CD, and monitoring frameworks. I thrive in cross-functional teams, translating business requirements into high-impact AI products.

Available to hire

I am a results-driven AI/ML Engineer with 4+ years of hands-on experience building and deploying production-scale ML, DL, and Generative AI solutions. I specialize in LLMs, RAG architectures, AI agents, NLP, and MLOps, with deep expertise in Python, PyTorch, TensorFlow, LangChain, LlamaIndex, and Hugging Face.

I have a proven track record delivering enterprise AI systems that automate workflows, support large-scale decision-making, and improve efficiency. I excel at building scalable data pipelines, deploying cloud-native AI apps on AWS and Azure, and applying LLMOps, CI/CD, and monitoring frameworks. I thrive in cross-functional teams, translating business requirements into high-impact AI products.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

AI/ML Engineer at Capital One

October 1, 2025 - Present

Implemented and deployed 10+ ML and Generative AI solutions using Python, PyTorch, TensorFlow, and Scikit-Learn; built RAG-based applications with LangChain, LlamaIndex, Hugging Face Transformers, and vector databases, reducing retrieval time by 45% and improving response relevance by 30%. Fine-tuned and optimized LLMs for customer support and enterprise knowledge management, boosting response quality by 35% and cutting manual effort by 40%. Designed AI Agents and Agentic AI workflows on AWS SageMaker and Amazon Bedrock, increasing operational efficiency by 30%. Engineered scalable data pipelines with SQL, Spark, Databricks, and Airflow processing 10M+ records, cutting data prep time by 50%. Implemented MLOps/LLMOps with MLflow, Docker, Kubernetes, and CI/CD, reducing deployment time by 60% and improving release reliability by 25%. Conducted model monitoring, A/B testing, and performance evaluation for systems delivering 50K+ predictions per month, improving stability by 25%.

Student IT Technical Support at Oklahoma Christian University

August 1, 2024 - August 1, 2025

Automated cloud account provisioning using Python and Azure CLI; developed SQL- and Python-based analytics to process application logs and telemetry data, improving issue detection time by 40%. Containerized JupyterHub and ML environments with Docker to reduce setup inconsistencies. Built FastAPI-based inference APIs for low-latency model serving and streamlined deployment. Orchestrated ETL workflows with Airflow, PySpark, and SQL, boosting dataset availability by 3x. Created SHAP-based explainability dashboards and technical documentation to improve model interpretability and adoption. Provided technical support for cloud, software, and AI-enabled academic environments, maintaining 95%+ service satisfaction.

AI/ML Engineer at Cybage Software

October 1, 2021 - December 1, 2023

Developed and deployed ML/DL models using Python, TensorFlow, PyTorch, Keras, and Scikit-Learn, improving prediction accuracy by 20%. Built end-to-end data pipelines (Pandas, NumPy, SQL, Apache Spark, Airflow) processing 5M+ records and reducing preparation time by 45%. Designed NLP solutions including text classification, sentiment analysis, and information extraction using SpaCy, NLTK, and Transformer models, reducing manual analysis effort by 60%. Developed predictive analytics, recommendation systems, and anomaly detection models, improving business decision accuracy by 25%. Built and deployed scalable AI/ML services using FastAPI, Flask, Docker, Kubernetes, AWS, and Azure, reducing deployment time by 40%. Implemented MLOps workflows using MLflow, model monitoring, and automated retraining pipelines, reducing maintenance effort by 35%. Collaborated with cross-functional teams to deliver 15+ production AI/ML applications, improving operational efficiency and business outcomes.