Looking to upskill your team?

Obsidi® Enterprise AI Engineering Fellowship

This immersive program is designed for senior developers with 3+ years of experience who want to deepen their AI expertise and apply it to real-world projects. Over an intensive 3-day weekend, you’ll work alongside mentors and peers to create impactful AI solutions.

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Join the Info Session on December 18 at 6:00 pm.

Program Snapshot

At a glance

3 Days
Fri–Sun, full-day
Live Sessions
Hands-on training
Expert Mentorship
1:1 and group
Certificate
From OBSIDI
CAD 1200
CAD 720
Introductory pricing
Eligibility
Senior Devs (3+ years)

Why this

  • Move from demos to deployable AI systems
  • Most teams stall at pilot because of unclear KPIs, ad hoc eval and slow sign offs
  • Get the frameworks and artifacts approvers need to escalate the process

What you’ll ship

  • A capstone tied to your environment
  • Architecture diagrams mapped to KPIs
  • Evaluation report (golden set + gates)
  • Governance pack (risk notes + templates)
  • Rollout plan for operational readiness

How it runs

  • Three live days –  workshops, labs, reading
  • Small cohort, high feedback
  • Secure environment
  • Exposure to the latest AI tools

Outcomes

Portfolio-ready AI project
Confidence applying AI at work
Certificate of completion
Expanded professional network
Practical exposure to current AI tools
Guidance from experienced mentors

You should apply if you

✓ Have 3+ years of experience as a software engineer or systems developer

✓ Are currently working in a senior IC, staff, platform, or engineering management role

✓ Want to build secure, evaluable, and production‑ready AI systems

✓ Are ready to lead in the AI‑powered enterprise, not just consume APIs

What you will learn

1
AI product thinking & KPI → architecture
  • Translate business goals into system KPIs
  • Map KPIs → SLIs/SLOs, ROI/TCO
  • Write an Architecture Intent memo
2
Structured generation & contracts
  • Function calling & JSON schemas
  • Guarded decoding & retries
  • When to use prompts vs RAG vs fine tuning
3
Retrieval systems (RAG)
  • Hybrid retrieval + rerankers
  • GraphRAG basics
  • Measure recall/quality vs latency
4
Agentic systems & MCP integration
  • Planner executor patterns
  • Safety rails & timeouts
  • Secure adapters to enterprise systems (MCP)
5
Gateway, EvalOps & governance
  • Routing & policies (multi model)
  • CI eval gates & telemetry
  • Privacy, risk notes & audit ready docs

Next Kickoff

Starts

February 20

Part of a 3 day intensive
(Feb 20–23, 2026)
Time
9:00 AM – 5:00 PM EST
Location
Online
Days
Fri • Sat • Sun
Cohort length
3 days
Format
Live
Seats
Limited

Frequently Asked Questions

Who is the AI Fellowship designed for?

The Fellowship is built for experienced engineers, software developers, and technical professionals who want to deepen their expertise in applied AI. The curriculum assumes familiarity with engineering workflows and focuses on advanced, practical AI development rather than beginner-level concepts.

What makes the Obsidi AI Fellowship different from other AI or machine learning programs?

Most AI courses focus on how to use existing models or depend heavily on pre-built tools. The Obsidi AI Fellowship teaches you how to architect, evaluate, and deploy AI models from the ground up. You work through real engineering workflows, build from scratch, and gain hands-on experience that translates directly into industry AI engineering roles.

Do participants learn to build models or only use pre-trained ones?

The fellowship does not focus on building or training foundation models from scratch. Instead, participants learn how to deploy, customize, and operationalize pre-trained LLMs to build real enterprise AI systems using techniques such as Enterprise RAG, Quantization and Semantic Caching.

What kind of projects will I work on?

Projects are designed to mirror real AI engineering problems. You’ll build, train, and deploy your own models, work through applied machine learning pipelines, and complete a capstone project that demonstrates an end-to-end AI solution. These projects challenge experienced engineers and strengthen your portfolio with production-focused work.

How does the Fellowship support career growth for experienced engineers?

The Fellowship helps engineers upskill into advanced roles involving model development, applied machine learning, and AI-driven product engineering. You’ll gain practical experience, receive guidance from industry instructors, and complete complex projects that reflect real engineering challenges.

What is the weekly schedule for the AI Fellowship?

The Fellowship runs three days per week from 9 AM to 5 PM. Each day includes a mix of lectures, hands-on model-building labs, technical deep dives, and guided project work. The pace is intensive and designed for engineering-level learners.

What is the expected workload outside of class hours?

Participants should expect to spend additional time outside the 9–5 sessions on project refinement, reading, assignments, and experimentation with models. The workload mirrors real AI development cycles and encourages iterative model improvement.

Will I receive a certification at the end of the Fellowship?

Yes. Participants who complete all required modules and the final capstone project will receive an Obsidi AI Fellowship Certification. This certification validates that you’ve completed an advanced, project-based AI program focused on building and deploying your own models.

What curriculum does the Fellowship follow?

The Fellowship curriculum includes:

  • Foundations of AI and machine learning
  • Deep learning and neural network architecture
  • RAG Systems Engineering
  • Agentic & Multi-Agent Systems Engineering
  • MCP & Secure Adapters
  • Governance & Compliance Engineering
  • Performance & Cost Engineering
How is this program different from traditional AI bootcamps?

Most bootcamps focus on theory or surface-level tooling. The Obsidi AI Fellowship is structured as a practical engineering program where participants learn how to build models from scratch, implement full pipelines, and work through real constraints faced in AI product development in just three days.