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AI Engineer focused on MLOps, AI infrastructure, cloud deployment, and DevSecOps for production LLM systems.

Dallas, Texas

Available for AI/MLOps & DevSecOps roles

AI Engineer · MLOps · DevSecOps

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Available for AI/MLOps & DevSecOps roles

Hi, I'm Anuoluwa Olutayo

AI Engineer·

I take AI and LLM systems from a working build to a release a team can trust. The work is integration, cloud deployment, MLOps, and DevSecOps: CI/CD, containers, Kubernetes, monitoring, and the security controls around production.

Dallas, Texas

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01 / About

About Me

I take AI systems from development to reliable, secure production deployments.

Hey! I'm Anuoluwa Olutayo, an AI Engineer focused on the operational side of LLM systems: integrating them, deploying them, and keeping them reliable and secure.

The work is MLOps and DevSecOps. That means CI/CD, cloud infrastructure, containers, Kubernetes, automation, monitoring, and the security controls a production release actually needs.

I don't train models from scratch or publish research. I take an AI system that already has a path and make it deployable: environments, access, policy, observability, and a way back when a release goes wrong.

  • Kubernetes
  • Docker
  • Terraform
  • CI/CD
  • AWS
  • Helm
  • FastAPI
  • LangGraph
  • DevSecOps
View Resume

LLM Engineering

Integrate RAG and agent workflows into services that can be deployed, observed, and rolled back.

AI Infrastructure

Cloud environments, containers, and Kubernetes as the runtime a production LLM service actually needs.

MLOps & Deployment

CI/CD, promotion, monitoring, and a release path that survives the first incident.

DevSecOps

Identity, secrets, image scanning, and policy checks so a release fails closed instead of shipping risk.

5

Featured Systems

3

Cloud Platforms

6

Certifications

2

Leadership Roles

02 / Projects

Selected Work

Systems integrated, deployed, secured, and kept running — from the pipeline to production.

Cited answer
Which policy covers the refund window?
Refunds are accepted within 14 days. The source is the billing policy, page 3.
p.3 · lines 40–48route · documents

Reply with the source passage and a retrieval route

0.4s
Cited lookup
3
Retrieval routes
100%
Answers with sources

A RAG assistant that shows the source, not just the answer

Citation Desk

The problem

Most assistants return a fluent paragraph and hide whether it came from a file, the web, or the model's memory. That is the wrong tradeoff when the answer will be checked.

What I built

A retrieval pipeline routes each question to uploaded documents or a web fallback, streams the steps, and cites the file, page, and passage used to write the reply.

Impact

  • ▸Every answer ships with the passage it used, so a reviewer can check it without reopening the file.
  • ▸Low-confidence questions refuse instead of guessing.
  • ▸The same pipeline supports policy docs, runbooks, and internal wikis.
  • Next.js
  • FastAPI
  • LangGraph
  • pgvector
  • Python
Live demo Case study

All projects (5)

03 / Experience

Work Experience

Placeholder roles — replace these with the systems you have shipped.

  1. AI Engineer

    YOUR COMPANY

    Current

    2025 — Present

    Taking LLM systems from a working build to a secured, observable deployment other teams can run.

    • ▸Shipped a document assistant with citations, tracing, and a release gate.
    • ▸Put evaluation in CI so a prompt change cannot ship on a single example.
    • ▸Traced every tool call so failures are visible without reading raw logs.
    • Python
    • LangGraph
    • FastAPI
    • PostgreSQL
    • Docker
  2. MLOps Engineer

    YOUR COMPANY

    2024 — 2025

    Owned the path around existing models: packaging, promotion, rollout, and the monitors that say whether the service is healthy.

    • ▸Replaced a manual handoff with a pipeline that promotes one container digest.
    • ▸Kept a previous release ready so rollback did not depend on the person who packaged it.
    • ▸Tied latency and error alerts to the release that was actually live.
    • Docker
    • Kubernetes
    • GitHub Actions
    • MLflow
    • AWS
  3. DevSecOps Intern

    YOUR COMPANY

    2023 — 2024

    Supported CI, containers, and the checks that keep a service from shipping with known gaps.

    • ▸Added image scans and secret checks to a pipeline that previously only built.
    • ▸Documented the environment setup so the next deploy did not start from a laptop.
    • Docker
    • GitHub Actions
    • Terraform
    • Linux

Education

Replace this with your school, degree, and focus.

B.S. Computer Science

2021 — 2025

YOUR UNIVERSITY

YOUR EDUCATION — coursework in software systems, cloud infrastructure, and security.

04 / Skills

Technical Skills

The stack for taking AI systems into production. Edit the groups to match your depth.

LLM Systems

8
  • LangChain
  • LangGraph
  • RAG
  • Agents
  • Evals
  • FastAPI
  • Streaming
  • pgvector

MLOps

6
  • CI/CD
  • MLflow
  • Model serving
  • Rollouts
  • Registries
  • Release gates

Cloud & Infrastructure

6
  • AWS
  • Kubernetes
  • Helm
  • Docker
  • Terraform
  • Linux

DevSecOps

6
  • Image scanning
  • Secret management
  • IAM
  • Pipeline security
  • SBOM
  • Least privilege

Observability

5
  • Prometheus
  • Grafana
  • OpenTelemetry
  • Logging
  • Alerting

Languages

4
  • Python
  • TypeScript
  • Bash
  • SQL

05 / Leadership

Leadership

The work of getting AI systems shipped, documented, and safe for the next person to operate.

AI Platform Lead

YOUR COMMUNITY

2024 — 2025

Set the path from a working demo to something a team could deploy: environments, reviews, and a release checklist.

  • ▸Ran sessions that ended with a service in a container, not a notebook.
  • ▸Reviewed projects for deployability, access, and monitoring before features.

Mentor

YOUR ORGANIZATION

2023 — 2024

Helped peers get a service through CI and into a shared environment, and wrote down the setup so it was repeatable.

  • ▸Turned one-off deploy advice into a short runbook.
  • ▸Paired on pipeline failures, secret handling, and rollback drills.

06 / Certifications

Certifications

Formal study across cloud, MLOps, and secure delivery. Replace these with yours.

2025

YOUR CERTIFICATION — Kubernetes

YOUR ISSUER

Workloads, networking, and the controls for running a service on a cluster.

2025

YOUR CERTIFICATION — MLOps

YOUR ISSUER

Packaging, promotion, deployment, and monitoring for models already built.

2024

YOUR CERTIFICATION — DevSecOps

YOUR ISSUER

Pipeline security, image scanning, secrets, and least-privilege access.

2024

YOUR CERTIFICATION — Terraform

YOUR ISSUER

Infrastructure as code for the accounts and environments an AI service runs in.

07 / Contact

Get in Touch

Have a role, a platform, or a deployment to get into production? Send a note.

Let's Ship It Securely

Open to AI Engineer, MLOps, and DevSecOps roles. If the work is taking an AI system into a reliable production deployment, start here.

Accepting client projects

Need a product built, not a hire?

Also available for focused builds: LLM features, automation, and internal tools. Replace this with your studio link.

Visit the studio

  • Email

    you@example.com
  • Location

    Dallas, Texas

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Common Questions

The things people usually ask before getting in touch.

Full-time AI Engineer, MLOps, and DevSecOps roles — the work of integrating, deploying, securing, and operating AI systems. Edit this answer in the portfolio data.