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Certificate in AI Engineering and Intelligent Automation

Design and implement AI-driven automation solutions to improve engineering processes and operational efficiency.

Last updated 07/2026EnglishIntermediate

About this programme

The Certificate in AI Engineering & Intelligent Automation is designed for professionals who want to build intelligent systems that automate repetitive work, improve decision-making, and integrate AI into real business environments.

Throughout this programme, you'll learn how to design AI workflows, connect enterprise systems, build machine learning-powered automations, and deploy scalable solutions using industry-standard tools.

Rather than focusing only on theory, every module includes guided labs, instructor-led workshops, practical assignments, and a capstone project based on real-world business scenarios.

By graduation, you'll have a professional portfolio demonstrating your ability to build production-ready AI automation solutions.

Course outcomes

What you will be able to do on completion

This programme is built around capability, not coverage. By the end you will be able to:

01Map an engineering process end to end and identify where automation actually pays off
02Build rule-based and AI-driven automation pipelines that run unattended
03Apply machine learning to process data for prediction and anomaly detection
04Integrate automation with enterprise systems through APIs and event queues
05Design human-in-the-loop checkpoints for decisions that should not be automated
06Measure automation impact on cycle time, cost and error rate
07Monitor deployed automations and recover cleanly from failure
08Manage rollout, documentation and team adoption of a new automation
09Deliver a working automation solution assessed against a real process brief

Course content

8 modules · 60 sessions · 120 hours

Sessions run two hours each, live with an instructor. Each module closes with a graded build.

1.1 What intelligent automation actually meansPreview2 hrs

1.2 Where AI fits in an engineering workflowPreview2 hrs

1.3 Rule-based automation versus learned behaviour2 hrs

1.4 Data as the fuel of automation2 hrs

1.5 The automation maturity model2 hrs

1.6 Cost, risk and return on automation2 hrs

1.7 Tooling landscape overview2 hrs

1.8 Setting up your automation workspace2 hrs

2.1 Documenting a process as it really runs2 hrs

2.2 Process mining from system logs2 hrs

2.3 Identifying bottlenecks and rework loops2 hrs

2.4 Task-level time and effort analysis2 hrs

2.5 Scoring automation candidates2 hrs

2.6 Feasibility versus impact matrices2 hrs

2.7 Writing an automation brief2 hrs

2.8 Stakeholder interviews and buy-in2 hrs

2.9 Building the business case2 hrs

2.10 Workshop: mapping your own process2 hrs

3.1 Anatomy of an automation platform2 hrs

3.2 Recording and scripting a first workflow2 hrs

3.3 Working with structured inputs2 hrs

3.4 Handling documents and unstructured input2 hrs

3.5 Conditional logic and branching2 hrs

3.6 Scheduling, triggers and event handling2 hrs

3.7 Error handling and safe retries2 hrs

3.8 Build: an end-to-end unattended workflow2 hrs

4.1 Framing a process problem as an ML problem2 hrs

4.2 Preparing operational data2 hrs

4.3 Classification for routing and triage2 hrs

4.4 Regression for duration and load forecasting2 hrs

4.5 Anomaly detection in process telemetry2 hrs

4.6 Feature engineering from event logs2 hrs

4.7 Model selection and validation2 hrs

4.8 Interpreting model output for operators2 hrs

4.9 Avoiding drift in production data2 hrs

4.10 Project: a predictive process model2 hrs

5.1 Combining rules, ML and generative AI2 hrs

5.2 Document understanding and extraction2 hrs

5.3 Natural-language interfaces to a process2 hrs

5.4 Decision services and policy engines2 hrs

5.5 Orchestrating multi-step pipelines2 hrs

5.6 Queueing, concurrency and throughput2 hrs

5.7 Human-in-the-loop review design2 hrs

5.8 Audit trails and traceability2 hrs

5.9 Cost control at volume2 hrs

5.10 Project: an intelligent processing pipeline2 hrs

6.1 REST and event-driven integration patterns2 hrs

6.2 Authentication and secure credentials2 hrs

6.3 Working with ERP, CRM and ticketing systems2 hrs

6.4 Data contracts and schema change2 hrs

6.5 Idempotency and exactly-once concerns2 hrs

6.6 Integration testing strategy2 hrs

7.1 Instrumenting an automation2 hrs

7.2 Alerting on silent failure2 hrs

7.3 Operational dashboards and SLAs2 hrs

7.4 Governance, approval and compliance2 hrs

7.5 Documentation and runbooks2 hrs

7.6 Change management and team adoption2 hrs

8.1 Capstone build and mentor review2 hrs

8.2 Portfolio, resume and interview preparation2 hrs

Requirements

A laptop with internet access
Willingness to work through hands-on builds each module
No prior AI background required — the first module starts from fundamentals
Around four hours a week outside sessions for project work

Description

Design and implement AI-driven automation solutions to improve engineering processes and operational efficiency.
60 live sessions across 8 modules, 120 hours in total.
Every module closes with a graded build reviewed by a mentor.
Finish with a verifiable certificate recognised by our hiring partners.

Instructor

Dr. Neha Rao

Lead AI Instructor · TEVVO Academy

12,480 reviews41,200 students9 courses

Neha has spent a decade building machine learning systems in production, most recently leading an applied AI team. She teaches the way she works — starting from a real problem, then building the smallest thing that solves it.

Student feedback

4.9Course rating

78%

14%

Featured reviews

Ananya S.

Structured, practical and paced properly. The graded build at the end of each module is what made it stick.

Karthik R.

The mentor reviews were the most valuable part — real feedback on real work, not just a quiz score.

Meera J.

Genuinely current material and a sensible schedule alongside a full-time job.