Job Title

AI Engineer (Trainer) — Exponential Engineer ProgrammeLocation: Pune (Hybrid)Experience: 8–12+ YearsRole: 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 ResponsibilitiesAI 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 tracksExample 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 BFSIPreferred Certifications- CAIP or equivalent AI credential - AWS ML Specialty / Azure AI Engineer Associate - TOGAF (added advantage)