Applied AI & ML Engineer
Professional experience
My experience spans source-grounded LLM workflows, predictive models, evaluation systems, data and feature pipelines, APIs, and cloud delivery, with measured improvements in quality, efficiency, review effort, and decision support.
Experience
Dec 2024 to present
Data & Applied AI Analyst
- Built a 150-case AI evaluation harness with RAGAS, semantic-similarity and LLM-as-judge scoring, inspectable traces, and latency and cost gates, reducing unsupported summary claims by 60%.
- Owned label definition, contextual feature engineering, model comparison, calibration checks, and uncertainty-aware output design for an internally used predictive model that improved planning accuracy by 21%.
- Designed a reusable event and feature foundation with canonical entities, feature contracts, data-quality tests, lineage tracking, and read-only downstream consumption patterns.
Aug 2023 to Nov 2024
Data Scientist, Applied AI
- Contributed to production Python services and Dockerized integrations supporting financial analytics and data delivery.
- Built predictive ML prototypes with feature engineering, validation, scikit-learn, TensorFlow, and XGBoost.
- Contributed to document-intelligence and data workflows using Vertex AI, Cloud Functions, BigQuery, Python, SQL, and Power BI.
- 21%
- analysis efficiency
- 17%
- data consistency
- 26%
- query performance
Apr 2023 to Jul 2023
Capstone Data Scientist
- Implemented K-means and hierarchical clustering after data profiling and outlier analysis in R.
- Produced cluster profiles and policy-facing findings for the Proximity Measure Database.
Sep 2021 to Aug 2022
Co-Founder / Data Scientist
- Co-founded a software firm and delivered client applications, APIs, analytics, and data workflows.
- Managed project scope, implementation, client communication, and technical handoff across concurrent engagements.
Education
- Master of Data Science University of British Columbia
- BSc Computer Science & Engineering American International University-Bangladesh
Engineering range
Applied AI systems
LLM integration, RAG, agent and tool workflows, structured outputs, prompting, source grounding, human review, and evaluation.
Machine learning
Feature engineering, anomaly detection, ranking, calibration, temporal modeling, clustering, NLP, and reviewer feedback.
Software & data
Python, FastAPI, REST and WebSocket APIs, SQL, PostgreSQL, BigQuery, pipelines, data contracts, and user-facing integration.
Delivery & trust
MLOps practices, Docker, cloud deployment, CI, automated tests, observability, validation, approval boundaries, and technical documentation.