Senior AI/ML Engineer (Agentic LLM Systems)

Human Sagheer

Senior AI/ML Engineer Agentic AI • RAG • LLM Systems • Data Science

️Email: **************@*****.*** Phone: 862-***-**** Teaneck, NJ US Citizen LinkedIn: https://www.linkedin.com/in/humansagheerhere GitHub: https://github.com/humansagheerdev

PROFESSIONAL SUMMARY Senior AI/ML Engineer with 8+ years of experience building machine learning, generative AI and data-driven applications using Python. Experienced in designing and deploying LLM systems, RAG pipelines, agentic workflows and MLOps platforms across finance, healthcare, e-commerce and enterprise environments. Strong background in machine learning, NLP, data engineering, cloud infrastructure and scalable software development on AWS, Azure and GCP. SKILLS

Machine Learning, Generative AI & Data Science

Python, Machine Learning, Deep Learning, NLP, Statistical Analysis, Predictive Modeling, Feature Engineering, Feature Selection, Exploratory Data Analysis (EDA), Data Mining, Time Series Forecasting, A/B Testing, PyTorch, TensorFlow, Scikit-Learn, Generative AI, LLMs (Large Language Models), MCP (Model Context Protocol), RAG

(Retrieval-Augmented Generation), LangChain, LangGraph, LlamaIndex, Hugging Face, Prompt Engineering, BERT, OpenCV, XGBoost, LightGBM, Reinforcement Learning, Conversational AI, LLM Security Software Engineering & Data Platforms

FastAPI, Flask, Django, Node.js, REST APIs, Microservices, PostgreSQL, MongoDB, Redis, Elasticsearch, Apache Kafka, Pandas, NumPy, PySpark, Databricks

Cloud, MLOps & Infrastructure

AWS (SageMaker, Bedrock, Lambda, EC2, S3), Azure Machine Learning, Docker, Kubernetes, MLflow, Weights & Biases, Terraform, CI/CD, GitHub Actions, Plotly, Dash, Matplotlib, Seaborn, Power BI WORK EXPERIENCE

Origami Risk Chicago, IL

Senior AI Software Engineer January 2022 - June 2026 Origami Risk is a leading provider of cloud-based risk, insurance and claims management software solutions for enterprise clients. As a Senior AI/ML Engineer, led the design and deployment of production-grade AI systems, focusing on Agentic AI, RAG pipelines and scalable LLM-powered applications across enterprise platforms; key contributions include:

● Led the development and deployment of Agentic AI solutions using Python, LangGraph, LangChain and LlamaIndex, enabling autonomous workflows and intelligent automation across enterprise business processes.

● Built scalable Retrieval-Augmented Generation (RAG) systems using vector databases, Elasticsearch, FAISS, Pinecone and Chroma, improving knowledge retrieval accuracy and reducing search time across enterprise data sources.

● Developed LLM orchestration frameworks and prompt engineering workflows using LangChain, Claude AI, LangSmith and AWS Bedrock, improving response quality, consistency and observability.

● Designed AI-powered automation services using Python, FastAPI, and n8n, automating manual business workflows and reducing operational overhead across multiple enterprise processes.

● Developed fraud detection, anomaly detection and predictive analytics models using PyTorch, Scikit-Learn, XGBoost and LightGBM, improving risk identification and enabling earlier detection of operational anomalies.

● Implemented real-time data processing and monitoring pipelines using PySpark, Apache Kafka, Redis and Databricks, supporting high-volume event processing and near real-time anomaly detection.

● Built conversational AI applications using Hugging Face models, BERT, RAG architectures and vector search techniques to improve contextual understanding and enterprise knowledge access.

● Integrated Speech-to-Text (ASR) and Text-to-Speech (TTS) capabilities into AI applications, enabling voice-driven assistants and conversational workflows.

● Optimized distributed model training and experimentation using PyTorch Lightning, DeepSpeed, Ray and Weights & Biases, improving scalability and model development efficiency.

● Built end-to-end MLOps pipelines using MLflow, Kubeflow, Docker, Kubernetes and GitHub Actions, reducing deployment effort and improving model release consistency across environments.

● Developed full-stack AI applications using FastAPI, React, Next.js and TypeScript, delivering scalable user-facing platforms powered by machine learning and LLM technologies.

● Designed cloud-native microservices and infrastructure using Docker, Kubernetes, Terraform, and AWS services, ensuring reliable deployment, scalability and operational consistency.

● Used AWS SageMaker, Bedrock, Lambda, EC2, S3, Azure Machine Learning and GCP ML services to support large-scale model training, inference, experimentation, and deployment.

● Implemented model monitoring, explainability and AI governance practices using MLflow, LangSmith, SHAP, LIME and LLM security techniques, improving transparency, reliability, and compliance for production AI systems. Tech Used: Python, FastAPI, LangChain, LangGraph, LlamaIndex, Claude AI, AWS Bedrock, Hugging Face, BERT, RAG, Prompt Engineering, FAISS, Pinecone, Chroma, Elasticsearch, PyTorch, Scikit-Learn, XGBoost, LightGBM, PySpark, Apache Kafka, Redis, Databricks, ASR/TTS, MLflow, Kubeflow, Docker, Kubernetes, Terraform, GitHub Actions, AWS (SageMaker, Lambda, EC2, S3), Azure Machine Learning, GCP, LangSmith, SHAP, LIME SoftServe Austin, TX

AI/ML Software Engineer February 2019 - December 2021 SoftServe is a global IT consulting and digital services company specializing in software engineering, cloud, data and AI solutions for diverse industries. As an AI/ML Engineer, developed and deployed machine learning and data-driven systems, supporting end-to-end AI solutions across healthcare, e-commerce and enterprise domains; key contributions include:

● Developed end-to-end machine learning solutions using Python, Scikit-Learn, XGBoost, LightGBM and CatBoost, improving predictive analytics capabilities and supporting data-driven business decisions.

● Built deep learning models using TensorFlow, Keras and PyTorch for NLP, computer vision and classification problems across healthcare and e-commerce domains.

● Engineered NLP solutions using NLTK, SpaCy, Word2Vec and BERT for text classification, semantic search, document analysis and information retrieval.

● Designed search and retrieval systems using Elasticsearch, embedding-based similarity search, and ranking techniques, improving information discovery and reducing time spent locating critical business data.

● Developed computer vision applications using OpenCV, CNNs, ResNet and UNet for image classification, object detection and segmentation tasks.

● Built anomaly detection and statistical modeling solutions using machine learning algorithms and exploratory data analysis techniques to identify operational risks and behavioral patterns.

● Implemented MLOps workflows using MLflow, Airflow, Docker and CI/CD pipelines, accelerating model deployment and improving reproducibility across development and production environments.

● Developed batch and streaming data pipelines using Python, SQL, Apache Kafka, Redis, Pandas, NumPy and PySpark, enabling reliable processing of large-scale analytical and operational datasets.

● Built backend services and REST APIs using Flask, Node.js, and Express.js to integrate machine learning capabilities into production applications.

● Created analytical dashboards and model explainability solutions using Power BI, SHAP and statistical reporting techniques to improve stakeholder visibility and trust. Tech Used: Python, Scikit-Learn, TensorFlow, Keras, PyTorch, XGBoost, LightGBM, CatBoost, NLTK, SpaCy, Word2Vec, BERT, OpenCV, CNN, ResNet, UNet, Elasticsearch, Flask, Node.js, Express.js, REST APIs, SQL, Pandas, NumPy, PySpark, Apache Kafka, Redis, MLflow, Airflow, Docker, AWS SageMaker, Power BI, SHAP Upwork Remote

Freelance Developer January 2018 - February 2019

Worked as an independent freelancer, partnering with clients to design and build custom software and data-driven solutions across web and early-stage AI use cases. As a Freelance Developer, developed full-stack applications and integrated foundational machine learning features to support practical business needs; key contributions include:

● Developed full-stack web applications using Python, JavaScript, React, Node.js, Flask, and REST APIs, delivering custom business solutions for clients across multiple industries.

● Built backend services and modular API architectures using Flask and Express.js, supporting scalable and maintainable application development.

● Designed and managed relational and NoSQL databases using PostgreSQL, MySQL and MongoDB, ensuring efficient data storage and retrieval.

● Implemented data processing, reporting and analytics workflows using Python, SQL, Pandas, NumPy and statistical analysis techniques to support business decision-making.

● Developed interactive visualizations and reporting solutions using Matplotlib, Plotly, Dash, and D3.js, improving data accessibility and stakeholder insights.

● Integrated machine learning models using Scikit-Learn and predictive analytics techniques to provide forecasting, classification, and recommendation capabilities.

● Built real-time application features using Firebase, Redis and event-driven architectures, reducing application latency and improving user experience.

● Automated deployments and application delivery using Docker, CI/CD pipelines and cloud-hosted environments, improving reliability and release consistency.

● Optimized application performance through database tuning, caching strategies, profiling and code refactoring, improving application responsiveness and overall system scalability. Tech Used: Python, JavaScript, React, Node.js, Flask, Express.js, REST APIs, PostgreSQL, MySQL, MongoDB, SQL, Pandas, NumPy, Scikit-Learn, Matplotlib, Plotly, Dash, D3.js, Redis, Firebase, Docker, CI/CD EDUCATION

Bachelor’s Degree in Computer Science September 2014 - June 2018 Air University, PK

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