Custom AI - Software Engineer III - Sales & Service - Customer

CustomAI

Custom AI

Customer | S&S | Senior Consultant

About the Role

Deloitte is seeking a Full Stack CRM Architect to own the design and delivery of components of custom, AI-augmented Customer Relationship Management platforms. This role sits between hands-on engineering and solution architecture: you will lead module- and feature-level design decisions, build critical parts of the platform yourself, and provide day-to-day technical direction to a small delivery pod.

You will partner closely with Managers and Senior Managers/Architects to translate the engagement's overall architectural vision into working software, while mentoring Consultants and helping instill Deloitte's engineering standards on the ground. This is a strong next step for engineers ready to move from building features to owning the design of the systems those features live in.

Key Responsibilities

Architecture & Solution Design (Module Ownership)

Own the detailed design of specific modules or services within custom CRM platforms — including data models, API contracts, and front-end structures — in line with the overall architecture set by the Manager/Architect. May approve module-level design changes independently; changes affecting the overall architecture pattern require Architect/Manager sign-off.

Architect and build full-stack features using modern frameworks (React, Angular, Next.js) for frontend and Node.js or Python/FastAPI for backend, with an eye toward scalability and maintainability.

Apply and reinforce architecture governance standards and reusable microservice patterns within your workstream; identify where existing patterns should be extended.

Contribute technical analysis to build vs. buy decisions, including prototyping and comparative evaluation of candidate approaches.

Product & Delivery Leadership

Co-lead discovery sprints and user feedback sessions for your workstream; translate business pain points into well-formed backlog items and acceptance criteria.

Track adoption and business-outcome metrics for the features your pod delivers, not just delivery dates.

Help run innovation labs and internal hackathons, guiding Consultants through prototyping emerging CRM capabilities (AI agents, voice interfaces, personalization engines).

Build and help publish reusable components toward Deloitte's proprietary CRM accelerators and reference architectures.

Research emerging technologies (agentic frameworks, LLM orchestration tools, edge inference) and recommend where they apply to current engagements.

AI, GenAI & Agentic Integration

Design and build AI/GenAI features within CRM workflows — including lead-scoring models, predictive components, agentic workflow steps, and intelligent content generation.

Build RAG pipelines and LLM orchestration components (LangChain, LlamaIndex) grounded in CRM knowledge bases; implement semantic caching and query routing to reduce inference costs.

Apply AI cost-governance practices within your workstream: model cascading (routing simple queries to smaller models), token budget tracking, and prompt compression.

Deploy and tune inference-serving components (containerized vLLM, managed inference endpoints), applying batching and caching strategies under senior guidance.

Build conversational AI features (chatbots, virtual agents) that automate Tier-1 service cases, meeting latency SLAs (<500ms P95) via request batching and streaming.

Implement output-validation and hallucination-detection checks for GenAI features you own, escalating drift or quality concerns to the Architect/Manager.

Track AI-attributed metrics (pipeline velocity contribution, cost-per-interaction, model efficiency) for your workstream and feed them into program-level reporting.

• AI Implementation Imperatives — Applied at the Workstream Level

Area

What this level does

Data First

Implement data-quality pipelines and CRM hygiene checks (deduplication, completeness scoring, freshness SLAs) for your module ahead of any AI deployment.

Scalability by Design

Design your module's AI-serving components to scale horizontally from day one (autoscaling, async queues) in line with the program's overall pattern.

Cost Efficiency

Right-size models to task complexity within your workstream; implement semantic caching and monitor token spend against budget for the features you own.

Adaptable Architecture

Design your module with modular prompt layers, pluggable model backends, and configurable workflow steps so it can evolve without rework.

Change & Adoption

Build intuitive UX and instrument engagement metrics for your features; surface adoption friction to the Manager/Architect and feed it into the roadmap.

Ethical AI

Implement bias-detection and fairness checks for predictive models in your workstream; flag issues to the engagement's model-governance cadence.

Integration

Design and build integrations with legacy systems, ERPs (SAP, Oracle, Microsoft Dynamics), MDM platforms, and third-party tools using REST, SOAP, GraphQL, and event-driven architectures (Kafka, RabbitMQ).

Lead integration of your workstream's CRM components with marketing automation platforms, communication APIs (Email, SMS, WhatsApp), and data lake/warehouse environments (Snowflake, Databricks).

Database Architecture & Performance

Design relational and non-relational data models for your module, optimized for high-volume customer interaction data; apply indexing, partitioning, and materialized views for query performance.

Lead performance-engineering tasks within your workstream: query plan analysis, connection pooling, read-replica routing, and cache-aside patterns (Redis/Memcached).

Apply SQL (PostgreSQL, SQL Server) and NoSQL (MongoDB, Cassandra) technologies, choosing polyglot persistence approaches where appropriate.

Security, Compliance & Cloud Engineering

Ensure your workstream's solutions adhere to data-privacy regulations (GDPR, CCPA, HIPAA) and enterprise security