Senior Machine Learning Engineer with Production Expertise

Mo Imran

MACHINE LEARNING ENGINEER

***********@*****.*** +1-249-***-**** Toronto, ON,

Profile

Senior Machine Learning Engineer with extensive experience designing, building, and deploying scalable machine learning systems in production environments. Strong expertise in Python, deep learning frameworks, large language models, MLOps, data engineering, and cloud solution architecture. Proven ability to design experiments, define evaluation metrics, optimize model performance, and collaborate cross-functionally with engineering, product, and business stakeholders. Master's degree in Computer Science with applied experience in ML research and production Al systems. KEY COMPETENCIES

Programming Languages

Python, R, SQL, Scala

AI AUTOMATION

Workflow Automation, Model Orchestration, Tool

Calling, Multi-Step Reasoning Systems

Cloud & Architecture

Azure, Cloud Solution Architecture, Scalable ML

Systems

Frameworks

PyTorch, TensorFlow, Triton Inference Server,

TorchServe

Experimentation & Performance Analysis

A/B Testing, Hypothesis Testing, Error Analysis,

Performance Monitoring

MLOps

Experiment Tracking, Model Versioning, CI/CD for

ML, Model Deployment, LLM Evaluation

Machine Learning

Machine Learning, Deep Learning, NLP, Computer

Vision

Methodologies

Agile, Scrum, Stakeholder Management, Product

Adoption, Business Acumen

Data Engineering

Large-Scale Data Processing, Embedding Models,

Vector Databases, Context Retrieval

PROFESSIONAL EXPERIENCE

Senior Applied Machine Learning Engineer, Eagis,Inc.

•Architect and maintain scalable machine learning systems and large language model applications using Python, PyTorch, and TensorFlow in production environments.

•Design and run structured experiments, select appropriate evaluation metrics, and implement model validation frameworks to ensure performance and reliability.

09/2022 – Present

•Build and deploy large language model solutions including prompt engineering, fine-tuning, adaptation strategies, and retrieval-augmented generation pipelines.

•Develop agentic systems with tool calling, multi-step reasoning workflows, and LLM evaluation frameworks.

•Implement MLOps best practices including experiment tracking, model versioning, CI/CD for ML, and automated deployment using Triton and TorchServe.

•Work with large, complex, and unstructured datasets while optimizing trade-offs between model quality, inference speed, infrastructure cost, and system complexity.

•Collaborate with engineers, researchers, product managers, designers, and business stakeholders to drive product adoption and Al feature growth.

•Utilize Azure cloud architecture for scalable deployment and production-grade ML infrastructure.

ML Systems Engineer, DarwinAl

•Develop maintainable and scalable ML systems in Python and SQL for large-scale structured and unstructured datasets.

•Build deep learning models for NLP and computer vision using PyTorch and TensorFlow.

01/2020 – 08/2022

•Design experimentation frameworks to evaluate model performance using clearly defined metrics and validation strategies.

•Implement embedding models, vector databases, and context retrieval strategies for intelligent search and generative Al use cases.

•Apply MLOps tooling including automated testing, experiment tracking, CI/CD pipelines, and model deployment workflows.

•Optimize model architecture considering performance, cost efficiency, latency, and scalability within Azure cloud environments.

•Partner with cross-functional teams to translate business requirements into production-ready machine learning solutions.

Associate Al Engineer, LEMAY.ΑΙ

•Design and implement machine learning and deep learning solutions using Python, R, SQL, and Scala.

•Develop data engineering pipelines for preprocessing large, complex datasets to support predictive analytics and NLP applications. 07/2016 – 12/2019

•Support research prototypes involving large language models, model adaptation techniques, and benchmarking workflows.

•Assist in containerization, deployment automation, and cloud-based integration of ML systems.

•Contribute to Agile and Scrum-based development cycles, working closely with product and engineering teams.

•Communicate technical concepts clearly to technical and non-technical audiences through documentation and presentations. EDUCATION

MS COMPUTER SCIENCE, UNIVERSITY OF SIALKOT

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