Looks like you have JavaScript disabled. For the full Twine experience, you will need to re-enable it.

Hi, I'm Ali Khan Mohammed Khalid, an AI/ML Engineer based in New Jersey, USA, with over 3 years of experience building and deploying ML and deep learning solutions across network intelligence, customer analytics, and enterprise AI applications. I specialize in end-to-end ML pipelines, NLP systems, and predictive modeling using Python, TensorFlow, XGBoost, and Hugging Face Transformers. I have hands-on experience operationalizing models with automated monitoring, feature pipelines, and retrieval-augmented generation systems. I hold an MS in Data Analytics and focus on delivering AI solutions that drive measurable operational and business outcomes.…Hi, I'm Ali Khan Mohammed Khalid, an AI/ML Engineer based in New Jersey, USA, with over 3 years of experience building and deploying ML and deep learning solutions across network intelligence, customer analytics, and enterprise AI applications. I specialize in end-to-end ML pipelines, NLP systems, and predictive modeling using Python, TensorFlow, XGBoost, and Hugging Face Transformers. I have hands-on experience operationalizing models with automated monitoring, feature pipelines, and retrieval-augmented generation systems. I hold an MS in Data Analytics and focus on delivering AI solutions that drive measurable operational and business outcomes.

Ali Khan Mohammed Khalid

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





Hi, I'm Ali Khan Mohammed Khalid, an AI/ML Engineer based in New Jersey, USA, with over 3 years of experience building and deploying ML and deep learning solutions across network intelligence, customer analytics, and enterprise AI applications. I specialize in end-to-end ML pipelines, NLP systems, and predictive modeling using Python, TensorFlow, XGBoost, and Hugging Face Transformers. I have hands-on experience operationalizing models with automated monitoring, feature pipelines, and retrieval-augmented generation systems. I hold an MS in Data Analytics and focus on delivering AI solutions that drive measurable operational and business outcomes.…Hi, I'm Ali Khan Mohammed Khalid, an AI/ML Engineer based in New Jersey, USA, with over 3 years of experience building and deploying ML and deep learning solutions across network intelligence, customer analytics, and enterprise AI applications. I specialize in end-to-end ML pipelines, NLP systems, and predictive modeling using Python, TensorFlow, XGBoost, and Hugging Face Transformers. I have hands-on experience operationalizing models with automated monitoring, feature pipelines, and retrieval-augmented generation systems. I hold an MS in Data Analytics and focus on delivering AI solutions that drive measurable operational and business outcomes.

Available to hire

Hi, I’m Ali Khan Mohammed Khalid, an AI/ML Engineer based in New Jersey, USA, with over 3 years of experience building and deploying ML and deep learning solutions across network intelligence, customer analytics, and enterprise AI applications.

I specialize in end-to-end ML pipelines, NLP systems, and predictive modeling using Python, TensorFlow, XGBoost, and Hugging Face Transformers. I have hands-on experience operationalizing models with automated monitoring, feature pipelines, and retrieval-augmented generation systems. I hold an MS in Data Analytics and focus on delivering AI solutions that drive measurable operational and business outcomes.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Intermediate

Intermediate

Language

English

Fluent

Work Experience

AI/ML Engineer at Juniper Networks

January 1, 2025 - Present

Led end-to-end ML pipelines for network telemetry data using Python and scikit-learn, improving anomaly detection accuracy by 28% and enabling near real-time visibility into distributed enterprise infrastructure behavior. Developed predictive maintenance models with TensorFlow and deep learning architectures to flag potential network equipment failures, reducing unplanned downtime by 22% through automated alerting workflows. Designed transformer-based NLP pipelines to classify and prioritize system log alerts, reducing manual triage effort and improving incident response consistency in high-volume environments. Streamlined feature engineering and data preprocessing workflows, cutting model training cycle time by 35% while preserving reproducibility across experimentation and production. Built a retrieval-augmented generation assistant grounded in internal docs using Hugging Face Transformers and LangChain, and implemented model monitoring and automated retraining to detect drift as tra

AI/ML Engineer at Tiger Analytics

April 1, 2021 - July 1, 2022

Developed customer propensity prediction models using XGBoost and ensemble techniques, increasing campaign targeting precision by 23% and enabling real-time decision scoring on large-scale marketing data platforms. Engineered scalable feature pipelines in Python and SQL for multi-terabyte datasets, reducing feature computation latency and supporting frequent model retraining. Built transformer-based NLP pipelines with Hugging Face for sentiment analysis across millions of customer feedback records, feeding near real-time customer experience dashboards. Designed automated experimentation workflows with cross-validation, hyperparameter tuning, and performance benchmarking, shortening model development timelines by ~30% and improving generalization. Applied K-Means clustering, PCA, and imbalanced data techniques (SMOTE, class weighting) to surface hidden segments for retention and lifetime value initiatives. Deployed predictive models via containerized workflows with automated monitoring,

Associate AI Engineer at Quantiphi

February 1, 2020 - March 1, 2021

Contributed to building classification and regression models in Python, achieving 15–18% improvement in baseline accuracy through structured feature engineering and cross-validation. Developed Python-based data ingestion and preprocessing pipelines consolidating multiple enterprise data sources, reducing manual preparation and improving dataset quality for downstream modeling. Assisted in prototyping computer vision solutions using CNNs in TensorFlow and Keras, achieving over 85% classification accuracy on internal PoC tasks. Performed EDA with pandas, Matplotlib, and Seaborn, and applied PCA for dimensionality reduction to improve feature relevance and reduce training complexity on high-dimensional data. Participated in model evaluation cycles covering cross-validation, probabilistic metrics, and benchmarking, helping identify overfitting and driving iterative improvements across projects.