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I am Mohamed Arshad Parvez, an AI Engineer and Data Scientist based in the UK with around 5.1 years of experience working with GenAI, LLMs, NLP, and DL on structured and unstructured data. I enjoy turning complex data into reliable, scalable AI solutions using Python, PyTorch, and modern MLOps tooling. I love mentoring teams, exploring AI ethics and governance, and building robust end-to-end pipelines with LangChain, LangGraph, and multi-agent RAG architectures. I’m comfortable deploying on AWS/Azure, orchestrating workflows with Kubernetes and Docker, and delivering production-grade AI systems that deliver measurable business impact.…I am Mohamed Arshad Parvez, an AI Engineer and Data Scientist based in the UK with around 5.1 years of experience working with GenAI, LLMs, NLP, and DL on structured and unstructured data. I enjoy turning complex data into reliable, scalable AI solutions using Python, PyTorch, and modern MLOps tooling. I love mentoring teams, exploring AI ethics and governance, and building robust end-to-end pipelines with LangChain, LangGraph, and multi-agent RAG architectures. I’m comfortable deploying on AWS/Azure, orchestrating workflows with Kubernetes and Docker, and delivering production-grade AI systems that deliver measurable business impact.

Mohamed Arshad Parvez

Data Scientist, AI Engineer, Full Stack Developer, +4

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I am Mohamed Arshad Parvez, an AI Engineer and Data Scientist based in the UK with around 5.1 years of experience working with GenAI, LLMs, NLP, and DL on structured and unstructured data. I enjoy turning complex data into reliable, scalable AI solutions using Python, PyTorch, and modern MLOps tooling. I love mentoring teams, exploring AI ethics and governance, and building robust end-to-end pipelines with LangChain, LangGraph, and multi-agent RAG architectures. I’m comfortable deploying on AWS/Azure, orchestrating workflows with Kubernetes and Docker, and delivering production-grade AI systems that deliver measurable business impact.…I am Mohamed Arshad Parvez, an AI Engineer and Data Scientist based in the UK with around 5.1 years of experience working with GenAI, LLMs, NLP, and DL on structured and unstructured data. I enjoy turning complex data into reliable, scalable AI solutions using Python, PyTorch, and modern MLOps tooling. I love mentoring teams, exploring AI ethics and governance, and building robust end-to-end pipelines with LangChain, LangGraph, and multi-agent RAG architectures. I’m comfortable deploying on AWS/Azure, orchestrating workflows with Kubernetes and Docker, and delivering production-grade AI systems that deliver measurable business impact.

Available to hire

I am Mohamed Arshad Parvez, an AI Engineer and Data Scientist based in the UK with around 5.1 years of experience working with GenAI, LLMs, NLP, and DL on structured and unstructured data. I enjoy turning complex data into reliable, scalable AI solutions using Python, PyTorch, and modern MLOps tooling.

I love mentoring teams, exploring AI ethics and governance, and building robust end-to-end pipelines with LangChain, LangGraph, and multi-agent RAG architectures. I’m comfortable deploying on AWS/Azure, orchestrating workflows with Kubernetes and Docker, and delivering production-grade AI systems that deliver measurable business impact.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

Data Scientist at Grid Dynamics Inc.

August 1, 2025 - October 15, 2025

Developed an end-to-end AI/ML system to predict aircraft performance metrics and simulate test outcomes, reducing dependency on manual/physical test runs and cutting review effort by approximately 60%. Built and optimized predictive modeling pipelines using XG Boost, LightGBM, Gradient Boosting, scikit-learn, Pandas, and NumPy, achieving >90% accuracy on structured flight datasets. Performed feature engineering, cross-validation, hyperparameter tuning, and model selection to improve predictive reliability under varying flight conditions. Designed and executed A/B testing and evaluation experiments to validate model robustness and guide iterative improvements. Deployed models via Azure ML with model monitoring, drift detection, and automated feedback loops for continuous optimization. Collaborated with data engineering and aerospace domain teams to integrate predictions into operational decision systems, improving testing efficiency and cycle time. Effectively communicated complex AI/ML

Data Scientist / AI Engineer at Indium Software Inc.

July 1, 2020 - December 31, 2023

Implemented end-to-end ML pipelines and built hybrid retrieval-augmented generation (RAG) pipelines using XGBoost, LightGBM, CatBoost, and deep learning frameworks. Led NLP classification, ML Ops, governance, and risk modelling for enterprise AI deployments. Designed and deployed multi-agent RAG pipelines using LangChain and LangGraph (CT/Ops, orchestrators, and workflows). Implemented LLM-driven evaluation and monitoring, optimized retrieval quality with cross-encoder reranking, and migrated workloads to cloud platforms (AWS/Azure) with CI/CD. Built scalable pipelines with Pinecone vector DB, Weaviate, and self-hosted components; delivered 70% reduction in manual reviews and improved production reliability.

Senior Data Engineer at Neviton Software Inc.

January 1, 2024 - January 1, 2025

Implemented hybrid RAG pipelines (BM25, BM25+Llama indexing, and self-hosted Weaviate) to improve retrieval relevance. Built end-to-end data pipelines, including Airflow orchestration, CI/CD, and API wrappers for external systems. Led governance, compliance checks, and validation workflows; designed multi-agent RAG workflows for inter-agent coordination and dynamic task execution; aligned with enterprise data/privacy requirements and production-grade RAG optimizations.

Data Scientist AI Engineer (Retrieval-Augmented Generation) at Grid Dynamics Inc.

February 1, 2025 - August 1, 2025

Focused on Retrieval-Augmented Generation (RAG) pipelines, graph RAG, and agentic RAG with Transformer architectures. Implemented cross-encoder reranking, multi-agent orchestration, and improved RAG evaluation with governance and monitoring. Built scalable GenAI systems on AWS/Azure, integrating with external systems and event-driven automation to support production deployments.