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Enterprise AI Architect and MLOps Lead with 8+ years designing and scaling production Generative AI, LLM, and RAG systems across AWS, GCP, and Azure. I build agentic platforms, Kubernetes-based inference infrastructure, and multi-cloud MLOps pipelines focused on reliability, cost control, and governance. I’ve shipped end-to-end solutions using LangChain, LlamaIndex, LangGraph, fine-tuning, and vector-backed RAG—delivering 99.9% uptime and faster release cycles. I also lead engineering teams and advise on AI strategy and roadmaps, with availability for fully remote contract/freelance engagements.…Enterprise AI Architect and MLOps Lead with 8+ years designing and scaling production Generative AI, LLM, and RAG systems across AWS, GCP, and Azure. I build agentic platforms, Kubernetes-based inference infrastructure, and multi-cloud MLOps pipelines focused on reliability, cost control, and governance. I’ve shipped end-to-end solutions using LangChain, LlamaIndex, LangGraph, fine-tuning, and vector-backed RAG—delivering 99.9% uptime and faster release cycles. I also lead engineering teams and advise on AI strategy and roadmaps, with availability for fully remote contract/freelance engagements.

Ghulam Jillani

Data Scientist, Full Stack Developer, AI Engineer, +6

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Enterprise AI Architect and MLOps Lead with 8+ years designing and scaling production Generative AI, LLM, and RAG systems across AWS, GCP, and Azure. I build agentic platforms, Kubernetes-based inference infrastructure, and multi-cloud MLOps pipelines focused on reliability, cost control, and governance. I’ve shipped end-to-end solutions using LangChain, LlamaIndex, LangGraph, fine-tuning, and vector-backed RAG—delivering 99.9% uptime and faster release cycles. I also lead engineering teams and advise on AI strategy and roadmaps, with availability for fully remote contract/freelance engagements.…Enterprise AI Architect and MLOps Lead with 8+ years designing and scaling production Generative AI, LLM, and RAG systems across AWS, GCP, and Azure. I build agentic platforms, Kubernetes-based inference infrastructure, and multi-cloud MLOps pipelines focused on reliability, cost control, and governance. I’ve shipped end-to-end solutions using LangChain, LlamaIndex, LangGraph, fine-tuning, and vector-backed RAG—delivering 99.9% uptime and faster release cycles. I also lead engineering teams and advise on AI strategy and roadmaps, with availability for fully remote contract/freelance engagements.

Available to hire

Enterprise AI Architect and MLOps Lead with 8+ years designing and scaling production Generative AI, LLM, and RAG systems across AWS, GCP, and Azure. I build agentic platforms, Kubernetes-based inference infrastructure, and multi-cloud MLOps pipelines focused on reliability, cost control, and governance.

I’ve shipped end-to-end solutions using LangChain, LlamaIndex, LangGraph, fine-tuning, and vector-backed RAG—delivering 99.9% uptime and faster release cycles. I also lead engineering teams and advise on AI strategy and roadmaps, with availability for fully remote contract/freelance engagements.

Skills

Experience Level

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Intermediate

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Language

English

Fluent

Work Experience

Freelance Generative AI Architect and MLOps Consultant (Contract) at Independent / Upwork

September 1, 2025 - Present

Delivered contract engagements that take GenAI prototypes into production, covering architecture, evaluation, cost control, and deployment. Shipped a multi-agent orchestration service using LangGraph, FastAPI, and Kubernetes with tracing, guardrails, and human approval gates, reducing manual operations effort by ~55% for targeted workflows. Built a multi-provider LLM gateway with semantic caching and cost-aware routing to reduce LLM spend by ~40% using per-team metering and budget alerts. Migrated workloads to LoRA fine-tuned Llama 3 on vLLM with quantization and GPU autoscaling, cutting inference cost ~35% at equal task quality. Implemented RAG evaluation in CI/CD scoring faithfulness, answer relevance, and retrieval quality to block regressions before release, sustaining 99.9% uptime on a production AI SaaS.

Generative AI Architect and Senior Data Scientist at Nuclieos

March 1, 2023 - August 31, 2025

Architected and deployed production LLM and RAG pipelines using LangChain, LlamaIndex, and LangGraph across AWS and GCP, reducing average query response latency by ~40% versus prototypes. Designed multi-cloud MLOps infrastructure with Kubernetes and Docker for zero-downtime deployments and 99.9% uptime. Built CI/CD pipelines with GitHub Actions and MLflow, reducing release cycles from weeks to under 48 hours and enabling continuous A/B evaluation of LLM variants in production. Developed semantic search and document Q&A systems backed by vector databases to support enterprise onboarding and revenue expansion. Led a team of 6 engineers and data scientists across Agile sprints, establishing MLOps best practices, model governance, and reusable libraries adopted across 4 internal products. Implemented model observability and drift detection with MLflow, keeping production accuracy within ±2% of baseline through proactive retraining.

Sr. Data Scientist & Machine Learning Engineer at Nuclieos

January 1, 2022 - November 7, 2025

Led development of AI-driven predictive models using TensorFlow and PyTorch to enhance analytics and decision-making. Optimized PySpark/SQL/NoSQL data workflows; deployed AI solutions on AWS and GCP with Docker and Kubernetes. Drove SaaS/PaaS AI initiatives, incorporating CI/CD; managed a team of data scientists and engineers using Agile and ML Ops. Built web apps and APIs with Flask/Streamlit, integrating generative AI; advanced NLP capabilities and data visualizations with Matplotlib/Plotly/Power BI.

Sr. Data Scientist & Machine Learning Engineer at Nuclieos

January 1, 2022 - November 5, 2025

Led development of AI-driven predictive models using TensorFlow and PyTorch; optimized data workflows with PySpark and SQL/NoSQL; implemented and scaled AI solutions in AWS and GCP with Docker and Kubernetes; drove SaaS/PaaS AI initiatives and crafted CI/CD pipelines; directed data scientists and engineers with Agile/ML Ops; built web apps and APIs with Flask and Streamlit, integrating generative AI; integrated NLP and generative AI to boost user engagement; created visualizations with Matplotlib, Plotly, and Power BI; focused on generative AI and NLP using Keras.

Artificial & Machine Learning Engineer at Crypto Express

December 1, 2021 - December 1, 2021

Developed and refined ML algorithms with Python, TensorFlow, and Scikit-Learn; designed ETL data pipelines with PySpark; applied NLP and generative AI to various applications; engineered AI solutions using PyTorch; collaborated in Agile environments for rapid prototyping and deployment; maintained AI applications in production with Docker and GitHub Actions; applied MLflow and AutoML to streamline deployment and tuning; built cloud AI solutions on AWS and GCP for efficiency and scalability.

AI and Machine Learning Engineer at Crypto Express

July 1, 2020 - February 28, 2023

Delivered end-to-end ML solutions in Python, PyTorch, and scikit-learn, improving production model accuracy by up to 15% via systematic tuning and feature engineering. Architected scalable ETL and data pipelines with PySpark across AWS and GCP, reducing data preparation time by ~30% and enabling near-real-time analytics for trading signal generation. Integrated NLP and early generative AI into client-facing applications, automating manual document classification and reducing processing time from hours to minutes. Containerized and deployed 10+ models using Docker and CI/CD to achieve reliable releases and rapid rollback for risk mitigation. Used MLflow for experiment tracking and model selection to standardize reproducibility across the ML team.

Artificial Intelligence Engineer at Techlogix

June 1, 2018 - June 1, 2018

Designed and deployed models with TensorFlow and Keras to improve system efficiency and accuracy; developed data preprocessing and analysis scripts with Python, Pandas, and NumPy; implemented NLP for automated customer interactions; built AI-driven Flask web apps for performance and engagement; conducted model testing and optimization; collaborated with cross-functional teams to align AI with business goals; deployed on AWS and Azure for scalability; automated repetitive data tasks to reduce manual effort; engaged in ongoing AI research to apply cutting-edge methodologies.