iamneo (Formerly Examly)
Website:
iamneo.ai
Job details:
Role: AI Engineering Trainer — Phase 2 (The Auditor)
Level: Senior Trainer
Experience: 3+ years
About Iamneo
Founded in 2016 and now part of the NIIT family, iamneo is a rapidly growing, profitable B2B EdTech SaaS company revolutionizing tech talent upskilling, evaluation, and deployment. Our AI-powered platforms enable enterprises and educational institutions to build future-ready talent at scale.
We partner with leading corporates like Wipro, HCLTech, LTIMindtree, Virtusa, Tech Mahindra, and Hexaware, as well as 150+ top educational institutions including BITS Pilani, VIT, SRM, LPU, and Manipal.
About The Role
iamneo is looking for an
AI Engineering Trainer — Phase 2 (The Auditor) to deliver the advanced AI auditing and debugging phase of its AI Track. This role is ideal for someone with strong experience in QA, security testing, DevSecOps, incident response, and code auditing for AI-generated systems.
Key Requirements
5+ years in
QA engineering, security testing, DevSecOps, or senior debugging roles
Hands-on experience auditing
AI-generated codebases for bugs, security flaws, and logic defects
Experience handling
production incidents including live diagnosis, stakeholder communication, and postmortem creation
Strong practical exposure to
OWASP Top 10 and real web application security audits or pen testing
3+ years delivering security, debugging, or DevSecOps training
Comfortable running
incident simulations, adversarial labs, and hallucination-hunting exercises
Required Technical Skills
AI code auditing: anti-pattern taxonomy including fabricated APIs, logic hallucinations, security omissions, over-confidence, context collapse, stale knowledge, and spec drift
OWASP Top 10 applied to AI-generated code: SQL injection, XSS, IDOR, broken authentication, security misconfiguration, SSRF
OWASP ZAP: active/passive scanning, authenticated scan setup, API scans via OpenAPI, report generation
Security scanning automation: Bandit, Semgrep, Snyk, Trivy
Scientific debugging methodology: Observe → Hypothesise → Experiment → Conclude
Code archaeology: reading unfamiliar codebases, dependency graph tracing, static analysis
Structured logging: JSON logging, correlation IDs, Loki/ELK, Jaeger/Tempo
Database debugging: N+1 detection, EXPLAIN ANALYZE, slow query logs, connection pool sizing
Production incident response: SEV classification, 2-hour resolution drills, 15-minute stakeholder update cadence
Blameless postmortem: 5 Whys, fishbone RCA, timeline reconstruction, action ownership
Performance profiling: cProfile, Chrome DevTools, memory leak detection, connection pool exhaustion analysis
CodeRabbit & Cursor: advanced review rule configuration, multi-layer validation pipeline
Core Responsibilities
Design and deliver
AI Engineering Phase 2 (The Auditor) across Days 16–38 of the AI Track
Teach debugging fundamentals including hypothesis-driven debugging, code archaeology, structured logging, and failure diagnosis across services
Run
production incident response simulations including outage and data corruption scenarios
Lead the AI auditing block across performance profiling, memory debugging, AI anti-pattern detection, and security audits
Oversee
Project 2 — Full Codebase Audit, where learners inherit a broken AI-built codebase and systematically diagnose, secure, improve, and document it
Administer the
Hallucination Hunt Assessment and calibrate grading
Design and maintain adversarial lab environments and vulnerable applications
Calibrate the final timed assessment covering debugging, hallucination detection, and audit methodology
Preferred Profile
Strong investigator mindset with practical experience in debugging and security
Able to teach learners how to think critically about AI-generated systems
Comfortable in fast-moving, simulation-heavy, hands-on training environments
Click on Apply to know more.