AI Systems Training Specialist

AI Engineer (Trainer) — Exponential Engineer Programme

Location: Pune (Hybrid)

Experience: 8–12+ Years

Role: AI Engineer (Trainer)

We’re looking for an AI engineering expert who can teach, mentor, and guide teams in safely integrating AI into enterprise BFSI systems. This role goes beyond model development — it focuses on AI lifecycle management, governance, drift control, risk mitigation, and secure integration.

🔧 Key Responsibilities

AI Training & Curriculum Delivery

• Deliver modules on AI system behavior, lifecycle, and failure modes in BFSI contexts.
• Teach integration patterns for LLM APIs, predictive models, and AI services using REST, gRPC, and event‑driven architectures.

Governance, Risk & Compliance

• Explain AI drift (model + data), bias, and regulatory implications (GDPR, PCI DSS).
• Guide participants in embedding AI across SDLC stages — requirements, design, development, testing, and operations.

Secure AI Integration

• Teach prompt engineering, secure API consumption, OAuth2/OIDC authentication, and audit‑logging patterns.
• Demonstrate safe consumption of Azure OpenAI, AWS Bedrock, HuggingFace, and other enterprise AI platforms.

Enterprise Collaboration & Project Support

• Work closely with Full Stack, Data, and QA trainers to ensure AI fits properly into the systems.
• Support participants on real BFSI scenarios (credit risk, fraud detection) with human‑in‑the‑loop controls.
• Review capstone AI designs for safety, failure handling, and governance alignment.

Required Experience

• 8–12+ years of total engineering experience with 4–6 years in AI/ML systems.
• Hands‑on AI integration using APIs/SDKs in production environments.
• Experience in BFSI or regulated industries with risk and compliance exposure.
• Strong Python for AI workflows and Java familiarity for enterprise integration.
• Experience with TensorFlow, PyTorch (conceptual), Azure Cognitive Services, AWS AI, or OpenAI APIs.
• Knowledge of AI observability — logs, metrics, drift detection, and monitoring.

Core Competencies

• AI system lifecycle & operational behavior
• BFSI regulatory awareness + AI governance
• Failure‑mode analysis, risk‑aware AI design
• Strong communication & facilitation
• Ability to simplify complex AI concepts for senior engineers
• Collaboration across application, data, and architecture tracks

Example Deliverables

• AI lifecycle + integration training modules
• Hands‑on labs for secure AI consumption and drift monitoring
• Capstone artefacts: integration design, governance controls, failover strategies
• Reference architectures for enterprise AI in BFSI

Preferred Certifications

• CAIP or equivalent AI credential
• AWS ML Specialty / Azure AI Engineer Associate
• TOGAF (added advantage)