This programme prepares experienced engineers to operate in a Forward Deployed Engineer (FDE) capacity, working closely with business stakeholders to identify problems, design AI-driven solutions, and deliver working systems in enterprise environments. The focus is on bridging engineering depth with consulting-oriented execution, where technical decisions are aligned to real business outcomes.
Participants navigate the full lifecycle of enterprise AI initiatives: from discovery and use-case framing to
architecture design, implementation, and performance evaluation. The programme emphasises decision-making in ambiguous environments where trade-offs between cost, scalability, security, and feasibility are common.

WHO SHOULD ATTEND

• Solution Architects, Tech Leads, and Senior Developers transitioning into FDE or consulting-oriented AI delivery roles
• Engineers expected to engage with stakeholders, define AI use cases, and own end-to-end solution delivery
• Professionals in enterprise environments where system design, business alignment, and deployment responsibility intersect

Experience: 8–15 years required. Not suitable for beginners or freshers.

PRE-REQUISITES

• Strong proficiency in Python and backend development
• Experience with distributed systems, APIs, and service-based architectures
• Working knowledge of AWS or Azure
• Understanding of data pipelines, data storage, and processing concepts
• Familiarity with system design principles and architecture patterns

K E Y O U T C O M E S

• Evaluate business problems and frame them into AI-solvable use cases with clear scope, constraints, and success criteria
• Apply structured decision frameworks to select appropriate solution approaches — RAG, agents, fine-tuning, or ML
• Design enterprise-grade AI architectures considering scalability, security, integration, and operational constraints
• Build and deploy end-to-end AI systems including data pipelines, RAG workflows, and agent-based solutions
• Assess system performance using evaluation, monitoring, and debugging techniques
• Operate agentic AI systems using LangChain, LangGraph, and multi-agent coordination with
  governance guardrails
• Integrate AI systems into enterprise environments via APIs, CI/CD, containerisation, and DevOps
  workflows
• Structure solution narratives and present architecture decisions to stakeholders with ROI framing and business value articulation