AI engineer Trainer

AI Engineer (Trainer) Exponential Engineer Programme

Location: Pune (Hybrid)

Experience: 812+ Years

Role: AI Engineer (Trainer)

Were 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 eventdriven 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 auditlogging 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 humanintheloop controls.
• Review capstone AI designs for safety, failure handling, and governance alignment.

Required Experience

• 812+ years of total engineering experience with 46 years in AI/ML systems .
• Handson 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
• Failuremode analysis, riskaware 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
• Handson 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)