Job Description
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, g RPC, 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 Open AI, AWS Bedrock, Hugging Face, 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 Tensor Flow, Py Torch (conceptual), Azure Cognitive Services, AWS AI, or Open AI 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)
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, g RPC, 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 Open AI, AWS Bedrock, Hugging Face, 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 Tensor Flow, Py Torch (conceptual), Azure Cognitive Services, AWS AI, or Open AI 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)
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