AI & MLOps EngineerBS Artificial IntelligenceHands-on MLOpsReproducible Pipelines
Building scalable, reproducible AI systems with MLOps.
I build production ML systems end-to-end: data ingestion → training → evaluation → packaging → deployment → monitoring-ready handoff. My focus is on reproducibility (DVC + Git), automated delivery (CI/CD), containerized runtimes (Docker), orchestration (Airflow), and cloud object storage (AWS S3).
Reproducible ML
DVC + Git for data/model versioning, pinned environments, deterministic runs.
Pipeline Automation
Airflow DAGs and CI/CD to remove manual steps and prevent regressions.
Deployment-ready
Dockerized training/inference and cloud-ready artifacts (S3-backed storage).
About (hands-on)
Practical ML engineering: versioned data, automated pipelines, deployable artifacts.
About
I’m an AI & MLOps Engineer with a BS in Artificial Intelligence and hands-on experience building and shipping ML workflows.
I’ve completed an MLOps course and apply those practices in real projects: versioning datasets and models with DVC, keeping experiments reproducible, packaging training/inference into Docker images, and automating pipelines via GitHub Actions and Apache Airflow.
I prefer engineer-grade deliverables: clear repository structure, deterministic runs, environment pinning, and deployment-ready artifacts over theory-only demos.
What I optimize for
- Reproducibility over one-off runs
- Automation over manual runbooks
- Artifacts over screenshots
- Clear inputs/outputs per step
MLOps toolkit
- GitHub (reviews, CI, releases)
- DVC (data/model versioning)
- Docker (portable runtime)
- Airflow (orchestration)
- AWS S3 (artifact storage)
Skills
Each skill reflects what I’ve built, shipped, or automated—no tool-only lists.
Core AI & Machine Learning
Modeling, training, evaluation, and deployment fundamentals applied in real pipelines.
Machine Learning (Supervised & Unsupervised)
Built baselines, tuned models with structured evaluation, and packaged inference as repeatable pipelines.
Deep Learning
Implemented deep models with training loops, metrics tracking, checkpointing, and deployment-ready exports.
Artificial Neural Networks (ANN)
Used MLPs for tabular prediction with feature preprocessing, validation splits, and regression/classification metrics.
Convolutional Neural Networks (CNN)
Trained CNNs for vision tasks with augmentation, transfer learning, and repeatable evaluation runs.
Recurrent Neural Networks (RNN)
Applied sequence models for time-series/text patterns with careful batching, padding, and evaluation.
Computer Vision
Worked on end-to-end CV workflows: dataset prep, training, evaluation, and serving outputs in apps.
Predictive Modeling
Delivered prediction systems with clearly defined targets, baselines, error analysis, and deployment hooks.
Model Training, Evaluation & Deployment
Standardized train/eval steps, saved artifacts deterministically, and deployed models behind simple APIs.
Knowledge Representation & Reasoning
Modeled domain knowledge using structured representations and rule-based reasoning where ML wasn’t ideal.
MLOps & Production ML
Reproducible experiments, versioned data/model artifacts, automated delivery, and cloud-ready runtimes.
End-to-End MLOps Pipelines
Designed pipelines that connect data ingestion → validation → training → evaluation → packaging → deployment.
Git & GitHub (Version Control, Collaboration)
Used branch-based workflows, code reviews, and structured repos for maintainable ML systems.
DVC (Data & Model Versioning)
Versioned datasets/models, tracked pipelines with dvc.yaml, and reproduced experiments across machines.
CI/CD for ML Pipelines
Automated linting/tests, build steps, and artifact publishing with GitHub Actions for consistent releases.
Docker (Containerization)
Built Docker images for training/inference with pinned dependencies and predictable runtime behavior.
Apache Airflow (Workflow Orchestration, DAGs)
Orchestrated multi-step ML workflows with DAGs, scheduling, retries, and clear task boundaries.
AWS Cloud Storage (S3 Buckets)
Stored data and model artifacts in S3-backed remotes for durable, shareable pipeline outputs.
Reproducible ML Experiments
Pinned environments, tracked parameters, and ensured deterministic reruns for reliable comparisons.
Model Lifecycle Management
Managed model versions, evaluation gates, and promotion-ready artifacts from dev to deployment.
Data Science & Analytics
From raw data to insight: cleaning, EDA, visualization, and automated reporting.
Data Analysis & Advanced Data Analysis
Produced analysis notebooks and pipeline outputs that translate data patterns into actionable findings.
Data Cleaning & Preprocessing
Handled missing values, outliers, schema validation, and feature-ready transformations.
Exploratory Data Analysis (EDA)
Performed distribution checks, correlation analysis, leakage checks, and hypothesis-driven exploration.
API-based Data Collection
Built API ingestion scripts with pagination, retries, and normalized storage for downstream pipelines.
Statistical Analysis
Used descriptive stats and tests where needed to support modeling decisions and business insights.
Data Visualization
Created clear plots/dashboards to communicate trends, anomalies, and model behavior.
Business Insights Generation
Converted analysis into decisions by connecting metrics and outcomes to operational context.
Automated Reporting (CBC Report Generator)
Automated end-to-end report generation: ingest → clean → analyze → visualize → export deliverables.
Programming & Development
Engineer-first implementation skills for shipping reliable AI systems.
Python (Primary language for AI, ML, Automation)
Built ML pipelines, automation scripts, APIs, and data tooling with clean packaging and dependencies.
C++
Used for performance-focused coursework/projects and solid fundamentals in memory and runtime behavior.
Java
Implemented OOP systems and backend-style logic with strong typing and maintainable structure.
Kotlin
Built academic/conceptual mobile prototypes with modern language features and clean architecture.
Programming Fundamentals
Applied clean coding practices, modularization, and debugging discipline in production-style repos.
Data Structures & Algorithms
Used core DS&A for efficient data processing, workflow design, and scalable implementations.
Programming for AI
Implemented training/inference code paths, evaluation, and automation with practical ML engineering focus.
Software Development Fundamentals
Built maintainable systems with clear APIs, tests where appropriate, and deployment-friendly packaging.
AI Applications & Intelligent Systems
Applied AI systems for automation, assistants, and real workflows.
AI Agents Development for Business Automation
Built tool-using automations that call APIs, manage tasks, and generate structured outputs reliably.
Chatbot Development (Clinical Chatbot)
Implemented a clinically-oriented assistant with intent handling, safety-minded responses, and logging.
Customer Support Automation
Automated ticket triage and response drafting with deterministic workflows and escalation paths.
Lead Generation Automation
Automated lead capture/enrichment pipelines and pushed qualified leads into structured storage.
Data Management Automation
Built scripts and jobs to validate, move, and version data across environments and storage.
Intelligent Systems Design
Designed systems where ML, rules, orchestration, and APIs work together as a single product.
Web & Application Development
Interfaces and integrations that ship AI into real user workflows.
Clinical Website Development
Built user-facing clinical web experiences with clear UX and integration points for AI features.
Backend Logic Integration
Integrated model inference, data APIs, and workflow triggers behind clean server-side interfaces.
AI-powered Web Applications
Delivered full-stack prototypes where AI outputs are traceable, testable, and user-friendly.
User-Centric Application Design
Designed flows around user intent, fast feedback, and clear failure modes—especially for AI systems.
Mobile App Development (Academic & Conceptual)
Created conceptual mobile app designs/prototypes focused on UX and data flow, ready to implement.
Tools & Libraries
Daily drivers for data work, analytics, and integration.
Pandas
Built reproducible data cleaning/feature pipelines and analysis outputs with consistent schemas.
NumPy
Used efficient array operations for preprocessing, feature engineering, and numeric routines.
API Integration
Integrated third-party APIs with auth, rate-limit handling, retries, and clean data normalization.
Excel
Delivered stakeholder-friendly exports and quick analysis checks alongside automated pipelines.
SQL
Queried and modeled datasets for analytics and pipeline inputs with clean, auditable queries.
Tableau
Built dashboards that communicate business KPIs and trends from cleaned and modeled datasets.
Academic & Professional Strengths
How I work when building real systems.
Strong Foundation in Artificial Intelligence
Applied AI fundamentals pragmatically: choosing the right tool (ML vs rules) for each constraint.
Research-Oriented Problem Solving
Prototyped, evaluated, and iterated with evidence—then packaged results into production paths.
Hands-on Project Development
Delivered working systems with clear boundaries, not just notebooks or slideware.
Pipeline-Based Thinking
Designed workflows as composable steps with explicit inputs/outputs and reproducible reruns.
Automation Mindset
Automated repeatable engineering tasks: data refresh, training runs, packaging, and report generation.
Cloud-Aware ML Systems
Designed storage and execution with cloud primitives in mind (S3, containers, CI runners, scheduling).
Projects
Detailed, pipeline-first projects showing implementation, architecture, and outcomes.
Project
End-to-End MLOps Pipeline Template (DVC + CI/CD + Docker + S3)
A reproducible ML project template that turns experiments into shippable artifacts.
Problem statement
ML projects often fail to move beyond notebooks because data/model versions drift, environments change, and deployment artifacts are inconsistent.
Architecture / pipeline flow
- Define a repeatable project structure (data/ → src/ → models/ → reports/)
- Version datasets and model artifacts with DVC; store remotes in S3
- Train + evaluate via a deterministic pipeline entrypoint (config-driven)
- Build an inference image with Docker for identical local/CI/cloud runtime
- Automate checks and releases with GitHub Actions (lint, tests, build, artifacts)
Tools & technologies
PythonGitHub ActionsDVCDockerAWS S3Pandas / NumPy
Implementation details
- Structured the pipeline around explicit inputs/outputs so a run can be reproduced from a single commit + DVC state.
- Added CI gates for formatting/linting and a build step that produces a deployment-ready Docker image.
- Stored data/model versions in S3-backed DVC remotes to support collaboration and rollback.
Outcome / impact
- Reproducible experiments across machines and CI runners.
- Clear path from training code to a deployable inference artifact.
- Maintainable repo layout recruiters can audit quickly.
Project
Automated ML Workflow Orchestration with Apache Airflow
A scheduled DAG that runs training, evaluation gates, and batch scoring with retries and observability.
Problem statement
Manual runs don’t scale: teams need reliable scheduling, retries, and clear visibility into each step of the ML workflow.
Architecture / pipeline flow
- Ingest data from sources (API/files) and validate schema
- Preprocess + feature engineer to a versioned dataset snapshot
- Train candidate models and compute evaluation metrics
- Gate promotion based on evaluation thresholds
- Run batch inference and publish outputs (tables/reports/artifacts)
Tools & technologies
Apache AirflowPythonDockerDVCAWS S3
Implementation details
- Designed DAG tasks with clean boundaries and idempotent behavior so retries are safe.
- Used artifact versioning to ensure each run is traceable back to the exact dataset + params.
- Implemented evaluation gates to prevent regressions from being promoted.
Outcome / impact
- Reliable scheduled ML runs with clear step-by-step visibility.
- Reduced human error by removing manual ‘runbook’ steps.
- Easier handoff to production environments due to containerized execution.
Project
Business Automation AI Agents (Lead Gen + Support + Reporting)
Tool-using automation agents that connect APIs, data pipelines, and structured outputs.
Problem statement
Business workflows often rely on repetitive manual steps: collecting leads, triaging support, and producing periodic reports from raw data.
Architecture / pipeline flow
- Collect signals from APIs/forms and normalize into a structured store
- Enrich and qualify records using deterministic rules + model outputs
- Generate structured outputs (summaries, action lists, CSV/PDF exports)
- Automate recurring reporting (including CBC report generation)
- Log runs and provide clear handoff/escalation paths for edge cases
Tools & technologies
PythonAPI IntegrationPandasSQLAutomation scripting
Implementation details
- Designed agent workflows around verifiable steps (inputs → transformations → outputs), not free-form automation.
- Implemented robust data normalization to keep downstream automation predictable.
- Added export-ready reporting for stakeholders (tables + visuals) and automated scheduling hooks.
Outcome / impact
- Less manual effort for recurring operational tasks.
- Faster turnaround for reports and qualified lead lists.
- More consistent support triage with clearer audit trails.
Project
Clinical Chatbot + AI-powered Web App Integration
A user-facing clinical assistant integrated into a web experience with safe UX patterns.
Problem statement
Clinical assistants must be reliable and easy to use: users need clear flows, consistent answers, and safe escalation when uncertainty is high.
Architecture / pipeline flow
- User interface designed for fast, clear clinical workflows
- Backend logic to route intents, log interactions, and apply safety checks
- Model-assisted responses with guardrails and fallback behavior
- Deployment-ready packaging and environment configuration
Tools & technologies
AI-powered web app patternsBackend integrationPythonUser-centric design
Implementation details
- Implemented structured intent handling and response templates to keep interactions consistent.
- Added logging and clear UI affordances for uncertainty and escalation.
- Built the integration to be modular so the AI layer can be swapped without rewriting the UI.
Outcome / impact
- Improved usability for clinical-style interactions through clear UX and predictable flows.
- Better engineering hygiene: modular backend integration and deployment-ready structure.
- A demonstrable AI app with real-world constraints (safety, traceability, UX).
GitHub & Code Quality
Clean structure, reproducibility, and real pipelines that reviewers can run.
GitHub & Code Quality
The goal is simple: repos that are easy to run, easy to audit, and clearly show production ML thinking.
- Repos structured for reproducibility: deterministic entrypoints, pinned dependencies, and clear configuration.
- Pipelines are real: data → train → evaluate → package → deliver (not notebook-only).
- Readable folder structure and documentation so reviewers can quickly run and audit results.
- Preference for automation: CI checks, scripted workflows, and containerized execution.
What reviewers look for
Reproducibility
One command run, stable environments, and versioned artifacts.
Traceability
Each model ties back to data + parameters + evaluation.
Delivery
Docker images, CI gates, and a clear deploy path.
Certifications & Education
AI foundation + applied coursework for production ML and analytics.
Education
BS Artificial Intelligence
AI systems, ML, data, and applied engineering
Strong foundation across ML/DL, intelligent systems, and practical project development.
Certifications
MLOps Course
Coursework
Covered reproducible ML, deployment workflows, CI/CD, artifact versioning, and orchestration patterns.
Google Data Analytics
Google
Data cleaning, analysis, visualization, and stakeholder-friendly reporting.
Google Advanced Data Analytics
Google
Advanced analysis, modeling, and decision-focused insight generation.
Contact
Open to AI/ML engineering roles focused on production systems and automation.
Preferred work
- MLOps pipelines and orchestration
- Production ML + automation
- Data workflows and analytics systems
- AI apps with backend integration