AI Engineering
Grounded RAG, agentic workflows and production LLM applications.
I am Akif Hameed, an AI and DevOps Engineer in London. I build production AI systems and the cloud infrastructure around them.
AI ENGINEER / DEVOPS ENGINEERCLOUD ENGINEER / AGENTIC SYSTEMS

// FROM QUESTION TO PRODUCTION






Turning evaluated AI research into useful, observable products that people can actually use.






// CAPABILITIES
Four years across Linux systems, cloud delivery and applied AI. I work from research design to a deployed, observable service, bringing model work and infrastructure together.
Grounded RAG, agentic workflows and production LLM applications.
Multi-stage systems that retrieve, reason, validate and respond.
Container platforms, automated delivery and observable runtime systems.
Deep learning, anomaly detection and evaluated predictive pipelines.
$ initialise intelligence_stack
rag_pipeline = grounded
citations = validated
refusal_policy = safe
✓ system ready 128ms
RAG applications, LLM orchestration and evaluation systems that connect research capability with real user needs.
Explore the work →$ run agent_graph
planner ONLINE
retriever ONLINE
validator ONLINE
response GROUNDED
Structured AI workflows that plan, retrieve evidence, validate citations and apply safe refusal behaviour.
View case study →$ evaluate anomaly_models
AE complete
Deep AE complete
Denoising complete
VAE complete
PyTorch and TensorFlow research workflows, tuned model families and feature-group ablation for measurable comparison.
Read the research →$ terraform apply
aws_instance created
cluster healthy
service available
outputs published
AWS and GCP environments designed for secure deployment, predictable scaling and dependable operation.
View credentials →$ deploy --production
build passed
container published
kubernetes rolled out
health 200 OK
Docker, Kubernetes, Jenkins, GitHub Actions and GitOps workflows that make releases repeatable and observable.
View pipeline →$ serve product_stack
frontend React
api FastAPI
data PostgreSQL
status LIVE
Python, Django, FastAPI, JavaScript, TypeScript and React used to turn models into complete applications.
Explore projects →// THE PATH
From Pakistan to London. Four years progressing from systems administration through DevOps engineering and towards AI Solutions Architecture.
MASTER OF SCIENCE • COMPLETED
Awarded an MSc in Computer Science with Distinction. Dissertation research focused on anomaly detection in human-robot collaboration using hyperparameter-tuned autoencoder families and systematic feature-group ablation.
Built AWS environments, containerised applications, supported CI/CD pipelines and automated reliable release processes.
Maintained delivery workflows, supported Linux environments and improved repeatable deployment through automation and version control.
Administered Ubuntu systems, access controls, software updates, services, backups and day-to-day technical operations.
// SELECTED WORK
Applied AI and cloud systems built from research to working product.
KEEP SCROLLING, THE WORK STACKS UP
SYSTEM.ONLINE01 / STATUS: LIVE
MODEL.SERVING02 / STATUS: LIVE
03 / STATUS: DEPLOYED
04 / STATUS: RESEARCH
SCIENTIFIC.ML05 / STATUS: COMPLETE
// VERIFIED LEARNING
Cloud foundations validated through the AWS Certified Cloud Practitioner credential.
Artificial intelligence foundations and responsible AI services validated by AWS.
Practical Kubernetes foundations covering workloads, services and cluster operations.
Container fundamentals validated through hands-on Docker training.
TRANSFORMING FROM DEVOPS ENGINEER TO AI SOLUTIONS ARCHITECT
// THE PROOF
Real signals from real work. No vanity metrics.
Experience across systems administration, DevOps engineering and applied AI.
Real Land Registry transactions used in the HousePredict workflow.
Legislative sources indexed for the LexUK tenancy assistant.
Industry training across AWS, artificial intelligence, Docker and Kubernetes.
A completed postgraduate degree connecting advanced computing, AI research and production engineering. The dissertation developed and evaluated hyperparameter-tuned autoencoder models for anomaly detection in human-robot collaboration using UR5e robotic data.
// LET'S TALK
AI and DevOps Engineer in London, building grounded AI applications and production cloud systems. Open to meaningful opportunities and collaborations.
Review engagement options, use the form below or book an available 30-minute call securely through Cal.com.