Custom AI

**Customer S&S Consultant**

About the Role

Deloitte is seeking a Full Stack CRM Developer to help design, build, and deliver bespoke, AI-augmented Customer Relationship Management applications. This is a hands-on engineering role for someone early in their consulting career who wants to build modern, scalable customer engagement platforms — not configure off-the-shelf tools.

You will work under the guidance of Senior Consultants, Managers, and Architects to build features, integrate AI capabilities, and deliver high-quality, well-tested code across the full stack. This role is designed to build deep technical skills in CRM engineering and GenAI integration while developing the foundation for future architecture and leadership roles at Deloitte.

Key Responsibilities

1. Architecture & Solution Design (Contributor)

• Build components of custom CRM platforms — including data models, API endpoints, microservices, and front-end features — following the architecture patterns and standards set by the engagement’s architects.

• Develop full-stack features using modern frameworks (React, Angular, Next.js) for frontend and Node.js or Python/FastAPI for backend, under the direction of senior technical leads.

• Follow established architecture governance standards and reusable microservice patterns; raise opportunities for improvement to senior architects.

• Support build vs. buy evaluations by prototyping and testing candidate approaches as directed.

2. Product & Delivery Mindset

• Participate in discovery sprints and user feedback sessions; help translate business pain points into user stories and backlog items.

• Support a product mindset by helping track adoption and usage metrics for the features you deliver.

• Participate in innovation labs and internal hackathons, prototyping emerging CRM capabilities under guidance from senior team members.

• Contribute code, tests, and documentation to Deloitte’s CRM accelerators and reference architectures.

• Stay current on emerging technologies (agentic frameworks, LLM tooling, edge inference) and share relevant findings with the team.

3. AI, GenAI & Agentic Integration

• Build and test AI/GenAI features within CRM workflows — such as lead-scoring models, content generation, and chatbot integrations — as specified by the architecture team.

• Support development of RAG pipelines and LLM orchestration components (LangChain, LlamaIndex) under senior guidance.

• Implement token-usage tracking and basic semantic caching per the engagement’s established cost governance framework.

• Assist with deploying and testing inference-serving components, including containerized vLLM or managed inference endpoints.

• Build and test conversational AI features (chatbots, virtual agents), validating functionality against defined latency SLAs (e.g., <500ms P95).

• Support output-validation and hallucination-detection testing as part of GenAI QA processes.

• Help capture basic AI usage metrics (cost-per-interaction, latency, accuracy) for reporting to senior team members.

4. AI Implementation Fundamentals

Area

What this level does

Data First

Execute data-quality checks and CRM hygiene tasks (deduplication, completeness scoring) as defined by the team’s data standards, before AI features are deployed.

Scalability by Design

Build components following the team’s established autoscaling and async-queue patterns rather than introducing one-off solutions.

Cost Efficiency

Apply caching and token-budget practices defined by the engagement; monitor and report token spend for the features you own.

Adaptable Architecture

Build modular, configurable components (prompt layers, pluggable services) rather than hard-coded logic, per team design guidance.

Change & Adoption

Build intuitive UX per design specifications and help instrument usage/engagement metrics; surface adoption friction observed during testing.

Ethical AI

Apply bias-detection and fairness-testing checklists provided by senior architects when validating model outputs.

5. Integration Support

• Build integrations with legacy systems, ERPs, MDM platforms, and third-party tools using REST, SOAP, GraphQL, and event-driven patterns (Kafka, RabbitMQ), following designs provided by senior architects.

• Support integration of the CRM with marketing automation platforms, communication APIs (Email, SMS, Whats…