HIGAET AI Systems Engineering
Learn to architect scalable, observable AI platforms covering orchestration, memory, evaluation harnesses, and production operations through intensive engineering labs.
Duration
12 weeks · 5-7 hours/week
Level
Advanced
Delivery
Hybrid
Status
Open for enrollment
Why this technology matters.
AI systems engineering is the architecture behind AI platforms that scale — multiple services, orchestration, memory, evaluation, and operations working together. It matters now because single-service demos break under real load, long-running jobs, retries, and changing models, and teams need platforms that stay observable and reliable.
It is used to run agent workloads, queued jobs, and inference topologies for a support team or a retailer serving many users at once. It solves service boundaries, orchestration with retries, regression testing, and load-balanced inference with fallbacks. It does not fix a bad product idea or poor underlying data — orchestration does not rescue an assistant with nothing trustworthy to retrieve, and dashboards do not fix bad quality metrics.
By the end you will be able to build a multi-service AI platform with clear service and data boundaries, an orchestration layer for agents, queues, retries, and long-running jobs, and an evaluation harness with regression suites and quality gates plus a scalable inference topology.
Why this course exists
The gap is between one working service and a platform that orchestrates agents, remembers state, passes quality gates, and survives traffic spikes and failures. This course teaches the full Model → Prompt → Context → Retrieval → Tools → Agents → Evaluation → Security → Infrastructure → Production arc at platform depth — from orchestration and memory through evaluation harnesses to load-balanced, observable production operations.
Know exactly what you're signing up for.
Who is this for
Prerequisites
- Comfortable with Python, APIs, and distributed services
- Familiarity with LLM APIs and agents
- Basic knowledge of load balancing and observability
Technologies & tools
Skills you'll gain
A 12 weeks arc, module by module.
- Module 01
Module 01 — Foundations: distributed AI system patterns and reference architectures
- Module 02
Module 02 — Core: orchestration, task graphs, and stateful agent runtimes
- Module 03
Module 03 — Memory: short-term, long-term, and shared context stores
- Module 04
Module 04 — Engineering: inference scaling, queues, and failure handling
- Module 05
Module 05 — Data: pipelines, feature stores, and feedback ingestion
- Module 06
Module 06 — Advanced: evaluation platforms and continuous quality gates
- Module 07
Module 07 — Production: observability, incident management, and cost governance
- Module 08
Module 08 — Capstone: production-grade AI platform design with operations runbook
Practical Training Flow
Learning → Guided Labs → Independent Practice → Industry Project → Capstone → Portfolio → Career Preparation. Practical hours are tracked alongside instructional hours and surfaced on the certificate.
Delivery as HIGAET Practical Training / Experiential Learning.
What you'll be able to do.
- Architect multi-service AI platforms with clear service and data boundaries
- Design orchestration layers for agents, queues, retries, and long-running jobs
- Develop evaluation harnesses with regression suites and quality gates
- Deploy scalable inference topologies with load balancing and fallbacks
- Integrate observability with traces, metrics, and cost attribution
- Evaluate scaling trade-offs across throughput, latency, and reliability targets
- Secure multi-tenant AI systems with isolation, quotas, and audit logging
- Optimize distributed AI workloads through batching, caching, and autoscaling
You will build.
Every project ships as HIGAET Practical Training / Experiential Learning — portfolio-ready work, not exercises.
- Project 01
Multi-service AI platform design
- Project 02
Agent orchestration with retries layer
- Project 03
Evaluation harness with quality gates
- Capstone
Observable scalable AI platform deployment
Speak the language first.
- Multi-service AI platforms
- Systems split into separate services for inference, orchestration, data, and evaluation, each with clear boundaries.
- Orchestration layers
- Coordination code that routes work across agents, queues, retries, and long-running jobs.
- Agent queues and retries
- Mechanisms that hold tasks in line and automatically retry failed steps without losing work.
- Long-running jobs
- Background tasks such as batch inference or index builds that run for minutes or hours with progress tracking.
- Conversational and task memory
- State stores that let agents remember history and context across steps and sessions.
- Evaluation harnesses
- Automated test setups with regression suites and quality gates that check AI behavior on every change.
- Quality gates
- Pass-or-fail thresholds in the release pipeline that block bad models or prompts from shipping.
- Inference topologies
- Arrangements of model servers with load balancing and fallbacks to stay fast and available.
- Observability for AI
- Logging, metrics, and traces that reveal latency, errors, and quality issues in production.
Fix, check, and go deeper.
Troubleshooting & common mistakes
Orchestrated jobs stall or duplicate work
Check queue visibility timeouts and idempotency keys, then fix retry logic so failed steps resume without re-running completed ones.
Memory grows stale or leaks across sessions
Inspect state-store keys and expiry rules, scope memory per user or task, and add pruning for old entries.
Eval harness passes but production quality drops
Compare the regression set against live traffic, add missing cases, and tighten quality-gate thresholds.
Inference overload during traffic spikes
Review load-balancer metrics and autoscaling rules, add caching and fallback models, and shed or queue excess load.
Service boundaries cause cascading failures
Trace failures across service logs, add timeouts and circuit breakers, and clarify ownership of each service contract.
Before you move on, you should be able to
- Architect a multi-service AI platform with clear service and data boundaries
- Design an orchestration layer for agents, queues, retries, and long-running jobs
- Develop an evaluation harness with regression suites and quality gates
- Deploy a scalable inference topology with load balancing and fallbacks
- Explain how observability reveals platform bottlenecks and quality drops
- Evaluate platform trade-offs in latency, cost, and reliability
Start your application.
Share a few details and a HIGAET advisor will reach out within one business day with next steps.
Common questions
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View CourseReady to start HIGAET AI Systems Engineering?
A 12 weeks course — AI & Generative Intelligence.