Senior ML Engineer - Generative AI & Data Science Leader

M Faysal

Lead ML Engineer Generative AI Engineer Data Scientist

*.******.****@*****.*** +1-343-***-**** Ottawa, Canada SUMMARY

Machine Learning Engineer with 6+ years of experience designing, building, and deploying scalable machine learning and data science solutions in production environments. Strong expertise in supervised and unsupervised learning, recommendation systems, fraud detection, credit risk modeling, and Generative AI applications using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Experienced in developing end-to- end ML pipelines, real-time feature engineering systems, and cloud-native model deployment workflows. Proven ability to translate business requirements into reliable, production-grade AI systems. PROFESSIONAL EXPERIENCE

Senior Machine Learning Engineer

Lama Consultancy Inc

•Design and deploy LLM-powered analytics solutions using Retrieval-Augmented Generation (RAG) for enterprise data querying.

01/2022 – Present

•Develop scalable machine learning pipelines for model training, validation, and inference.

•Implement model evaluation frameworks measuring accuracy, latency, and business performance metrics.

•Optimize embedding and retrieval strategies to improve semantic search relevance.

•Deploy containerized ML services using Docker and Kubernetes in cloud environments.

•Build monitoring and logging systems for production model performance and reliability.

•Collaborate with cross-functional teams including data engineering, product, and security.

Machine Learning Engineer

Integrate.ai

•Developed recommendation and ranking models using collaborative filtering and deep learning techniques.

01/2020 – 12/2021

•Built real-time feature engineering pipelines for low-latency inference.

•Implemented multi-objective optimization balancing relevance and business metrics.

•Designed and executed A/B testing frameworks for model experimentation.

•Deployed ML services via REST APIs and cloud-native microservices architecture.

•Maintained CI/CD pipelines for automated model testing and deployment. Associate Machine Learning Engineer

Helic & Co.

•Developed fraud detection models using gradient boosting and statistical learning methods.

01/2018 – 12/2019

•Built credit risk scoring models using generalized linear models and tree-based algorithms.

•Created ETL pipelines transforming raw transactional data into ML-ready datasets.

•Performed exploratory data analysis and feature engineering to improve model performance.

•Assisted in converting research prototypes into production-ready APIs.

•Generated reports and visualizations to support stakeholder decision-making. SKILLS

Technical Leadership and Strategy:

ML System Architecture, AI Platform Design, Technical Roadmapping, Cross-Functional Collaboration, Stakeholder Management, Production Ownership, Code Reviews, Mentorship, Model Governance, Responsible AI, AI Security and Privacy

Machine Learning and Data Science:

Supervised Learning, Unsupervised Learning, Gradient Boosting (XGBoost, LightGBM), Random Forest, Neural Networks, Deep Learning, Natual Language Processing (NLP), Recommendation Systems, Ranking Algorithms, Multi-Objective Optimization, Credit Risk Modeling, Fraud Detection, Churn Prediction, Demand Forecasting, Model Evaluation, Model Calibration, Feature Engineering Generative AI and LLMs:

Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Prompt Engineering, LangChain, Agentic AI Systems, Multi-Agent Systems, LLM Evaluation Frameworks, Hallucination Detection, Semantic Search, Embeddings, Vector Databases, AI Workflow Automation Programming and Analytics:

Python, SQL, R, Bash, TypeScript, Pandas, NumPy, Scikit-Learn, PyTorch Data Engineering and Pipelines:

Enterprise ETL Pipelines, Data Warehousing, Real-Time Feature Engineering, Batch and Streaming Pipelines, Informatica PowerCenter, Informatica IICS, Apache Spark, Apache Flink, Spring Batch Cloud and Big Data:

AWS, Azure, OpenAI, Funtions, ACR, Amazon S3, AWS Lambda, SageMaker, GCP, BigQuery, Databricks, Snowflake, Amazon Redshift

MLOps and Deployment:

Docker, Kubernetes, CI/CD, GitHub, Microservices Architecture, REST APIs, Model Deployment, Model Monitoring, Experimentation Platforms, A/B Testing Databases:

PostgreSQL, MongoDB, Redis, Elasticsearch

EDUCATION

Master of Science in Computer Science

University of Engineering and Technology

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