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I'm Ali Hussain, a results-driven AI/ML and MLOps engineer based in New York. I turn ambitious business needs into scalable ML systems by taking models from ideation to production, bridging gaps between data science experimentation and reliable engineering. With 5+ years of experience delivering end-to-end ML pipelines, retriever systems, and robust MLOps practices, I design LLM-enabled features, optimize performance, and collaborate across product, data, and engineering teams to drive measurable business impact.…I'm Ali Hussain, a results-driven AI/ML and MLOps engineer based in New York. I turn ambitious business needs into scalable ML systems by taking models from ideation to production, bridging gaps between data science experimentation and reliable engineering. With 5+ years of experience delivering end-to-end ML pipelines, retriever systems, and robust MLOps practices, I design LLM-enabled features, optimize performance, and collaborate across product, data, and engineering teams to drive measurable business impact.

Ali Hussain

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

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I'm Ali Hussain, a results-driven AI/ML and MLOps engineer based in New York. I turn ambitious business needs into scalable ML systems by taking models from ideation to production, bridging gaps between data science experimentation and reliable engineering. With 5+ years of experience delivering end-to-end ML pipelines, retriever systems, and robust MLOps practices, I design LLM-enabled features, optimize performance, and collaborate across product, data, and engineering teams to drive measurable business impact.…I'm Ali Hussain, a results-driven AI/ML and MLOps engineer based in New York. I turn ambitious business needs into scalable ML systems by taking models from ideation to production, bridging gaps between data science experimentation and reliable engineering. With 5+ years of experience delivering end-to-end ML pipelines, retriever systems, and robust MLOps practices, I design LLM-enabled features, optimize performance, and collaborate across product, data, and engineering teams to drive measurable business impact.

Available to hire

I’m Ali Hussain, a results-driven AI/ML and MLOps engineer based in New York. I turn ambitious business needs into scalable ML systems by taking models from ideation to production, bridging gaps between data science experimentation and reliable engineering.

With 5+ years of experience delivering end-to-end ML pipelines, retriever systems, and robust MLOps practices, I design LLM-enabled features, optimize performance, and collaborate across product, data, and engineering teams to drive measurable business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Data Scientist at Trusted Media Brands

April 1, 2024 - Present

Own end-to-end ML lifecycles across forecasting, retrieval, and generative AI, delivering 6 production systems serving +1M requests per day.

AI/ML Engineer at Vectora

August 1, 2022 - March 1, 2024

Designed and deployed retriever pipelines combining dense and sparse methods, improving top-5 retrieval recall by 12% while reducing average query latency by 18%. Led full-cycle fine-tuning of generative models using RLHF, aligning outputs with domain-specific style and factual consistency. Built automated evaluation framework for retrieval and generation components, establishing guardrails that blocked hallucinatory responses before reaching production. Scaled inferences serving in infrastructure to support concurrent requests across multi-tenant enterprise tenants, maintaining consistent latency under load. Collaborated with engineering teams to integrate LLM capabilities directly into product workflows, delivering features that reduced manual review effort for end users.

MLOps Engineer at Buzz Solutions

February 1, 2021 - July 1, 2022

Containerized machine learning models with Docker and orchestrated deployments on Google Kubernetes Engine (GKE), enabling reliable inference at scale. Developed REST APIs using FastAPI to expose trained models as production endpoints, integrating them with backend applications and frontend services. Implemented monitoring and logging for deployed models and infrastructure, tracking performance metrics to proactively identify and resolve system issues. Maintained SQL databases and optimized query performance, reducing average execution time by 30% and supporting cost-efficient cloud operations.