Production operations
I lead the flow from order intake through scheduling, build preparation, printing, post-processing, quality, packing and delivery. That means making capacity, labour, WIP, material and recovery decisions as conditions change.
Hi, I'm Alen P. Jose · Toronto
I build manufacturing systems that work better on the floor.
I started close to the machines—supporting applications, servicing equipment and planning production. As the operation grew, so did my responsibility. Today I lead the people, priorities, capacity, equipment and recovery decisions behind day-to-day delivery.
Open to production, operations and engineering leadership roles
5+ yearsAdditive manufacturing
4+ yearsProduction responsibility
2+ yearsPeople & operating leadership
End-to-endIntake through shipment
Broad ranges protect employer, customer and commercially sensitive information while showing the scale of responsibility.
01 · Expertise
I lead the whole production flow, not just one step in it.
The job is to keep work moving while protecting quality, equipment and delivery. That requires technical depth, but also calm prioritization, clear ownership and good information when the plan changes.
Additive engineering
My technical base spans HP MJF, Formlabs SLS and SLA, Markforged reinforced FDM/CFF and open-material extrusion. I connect application discovery and DFAM with build strategy, process limits, post-processing and field-service troubleshooting.
Reliability & quality
When output becomes unstable, I look beyond the immediate symptom. Preventive maintenance, error history, root-cause analysis, standard work and visible flow controls help turn one fix into a more reliable process.
Digital systems & applied AI
I use SharePoint, Power Automate and AI-assisted development to make operating information easier to capture and act on. My current AI work centres on retrieval, deterministic tool use and human review, while I build deeper capability in evaluation and security.
Process & tool coverage
HP MJF · Formlabs SLS / SLA · Markforged CFF · FDM · powder removal · bead blasting · vapor smoothing · dyeing · painting · assembly · SharePoint · Power Automate · Python · Odoo · SolidWorks / Solid Edge
AI-assisted workflows
Codex / Claude Code for supervised implementation, code analysis, debugging and review · Claude-assisted design for interface exploration and rapid prototypes · NotebookLM for source-grounded research and synthesis. I review, test and verify the resulting work.
02 · Career progression
The title changed after the work already had.
I joined Designfusion in an applications and field-service role. The operation kept pulling me deeper—first into planning and scheduling, then into people leadership and the decisions that determine whether work ships. Production Manager formalized that progression in July 2026.
01Jan - Apr 2020
Student Researcher
Centennial College · ARIES Lab
Additive manufacturing + structural validation
My first formal additive-manufacturing work was an industry-partnered aerospace study in Centennial College's ARIES Lab. I used topology optimization and FEA, designed test fixtures and compared simulation results with destructive testing.
02Jan 2021 - Jun 2026
Application Specialist
Designfusion Inc. · Toronto
Applications + field service + established production leadership
I joined to help customers find viable additive applications and support equipment in the field. By 2022, production planning, scheduling and equipment readiness were regular parts of the role. As volume grew, I took on daily team leadership and wider operating decisions.
03Jul 2026 - Present
Production Manager
Designfusion Inc. · Toronto
Production system + people + delivery
The July 2026 title made official an operating scope I had already carried for at least two years. Today I lead priorities, people, equipment readiness, materials, post-processing, quality and delivery recovery across the additive operation.
03 · Selected work
Selected systems built around real operating problems.
These examples sit at different stages: live operations, internal workflows, working prototypes and public learning builds. Each began with a practical constraint—not a technology looking for a use.
Why some evidence is generalized
I do not publish customer files, identifiable parts, production records, commercial measures or machine histories. When a screenshot would expose employer information, I use broad ranges and generalized diagrams. Public projects link to inspectable code and synthetic data.
01Intake
02Plan
03Build
04Finish
05Quality
06Deliver
The schedule is one control layer. Material readiness, equipment condition, labor, finishing capacity and recovery decisions determine whether work moves.
Operational responsibility
Scaling a multi-process additive operation
As demand grew, the challenge was not simply to add machine hours. Intake, build planning, finishing, maintenance, materials and shipping all had to move as one system.
- Role
- I set production priorities, maintained the master schedule, coordinated the technical team and led recovery planning when equipment or workflow disruptions put delivery at risk.
- Method
- I used visual work control, standard work, preventive-maintenance planning, material readiness and root-cause review across printing and finishing.
- Outcome
- The operation absorbed multi-year growth with clearer ownership, a steadier production rhythm and stronger control of quality and delivery.
Double-digitMixed equipment fleet
Five-figureAnnual part-volume band
End-to-endIntake through shipment
Working prototype
Additive Manufacturing Control System
Orders, files, part records and build decisions were spread across separate tools. I built a working model of how that information should connect before the company committed to a long-term platform.
- Role
- I mapped the relationships between orders, parts, builds, reprints and statuses, then translated them into role-based workflows.
- Method
- The prototype used manufacturing-specific data objects, controlled status changes and the real hand-offs between sales and production.
- Outcome
- It became a practical benchmark for evaluating commercial systems. File security, support and maintainability ultimately mattered more than deploying a self-built internal tool.
Working MVPRequirement benchmark
Role-basedWorkflow model
File safetyPlatform constraint
Operational workflow
Maintenance & Error Tracker
A service log cannot explain a recurring failure when errors, machine use, print history and maintenance are recorded separately. This workflow brings those histories together around one device.
- Role
- I defined how device, error, usage, print and maintenance records connect, then shaped a QR-based capture flow for use at the machine.
- Method
- QR entry, structured task records, technician notes, images and corrective-action history connect a current symptom with what happened before it.
- Outcome
- The result is a root-cause learning loop rather than a passive service log, preserving context between troubleshooting, corrective action and preventive maintenance.
QR accessPoint-of-work entry
Asset-centredConnected histories
RCA loopCorrective learning
Foundational learning build
UtilityOps Readiness Assistant
UtilityOps began with a practical question: which jobs are ready to proceed, and what is blocking the rest? The public build uses that question to explore retrieval without presenting an early learning project as production AI.
- Role
- I built a readiness interface that combines structured checks, document retrieval, blocker summaries, source references and an explicit human-review state.
- Method
- Deterministic checks handle known readiness rules; retrieval brings the supporting documents forward so a reviewer can see the evidence.
- Outcome
- The project established the retrieval foundations and exposed the next learning requirements: evaluation, security, observability and production operations.
Public repoInspectable evidence
RetrievalFoundational scope
SyntheticDemonstration data
04 · Systems Lab
The lab turns technical gaps into structured public work.
Building with AI is easy; understanding the systems behind it takes more discipline. I use the lab to slow down, test assumptions and learn the architecture, evaluation, security and operating trade-offs behind the tools.
AI Engineering Lab
A six-month public build program designed to replace surface-level familiarity with production-level understanding. The sequence covers architecture, retrieval and hybrid search, tool calling, evaluation, security, observability, deployment and cost.
Architecture · retrieval · evaluation · securityRolodex operational knowledge system
A prototype for keeping operational knowledge useful over time: timelines preserve events and context, structured details hold supporting evidence and tasks carry action. I am using it to study context design, generated views and system boundaries.
Next.js · Supabase · auth · context designFloor visibility & workstation cues
Two practical automation ideas: visual machine-state recognition for status visibility, and Raspberry Pi workstation reminders for process checks. Both remain concepts until sensing reliability, failure modes and value to operators are tested.
Computer vision · edge devices · human factors01Start with the workWatch how the job is really done and where it repeatedly breaks down.
02Make the system visibleClarify ownership, state, dependencies and the information people need.
03Strengthen the loopMake the next decision easier, faster and more repeatable.
05 · About
I learned systems thinking from the floor.
Real production is untidy. Priorities change, machines fail, information arrives late and experienced people make judgment calls that no process map fully captures. A useful system has to work in that reality, not just look clean in a presentation.
I am most useful where three views have to meet: the technician dealing with the immediate problem, the engineer deciding what should change and the leader weighing risk, cost and delivery. That is also how I approach software and applied AI—begin with the work and the people responsible for it, then choose the technology.
EngineeringBachelor of Engineering, Mechanical EngineeringMG University · WES Canadian equivalency
Mechanical designMechanical Engineering Technology: DesignCentennial College · High Honours
Technical credentialsHP MJF Field Service Engineer · CSWAEquipment service and mechanical design foundations
Professional developmentData Science FoundationsDSI, University of Toronto
Contact
Looking for someone who can connect the floor to the system?
I am open to production, operations and engineering leadership roles, along with conversations about additive manufacturing, digital operations and practical industrial AI.
Location
Toronto, Canada
Emailalenpjose@gmail.com
LinkedInin/alenpjose ↗
GitHubgithub.com/alenpjose ↗
RésuméDownload PDF ↓