Technical Anchor

Ford Credit IT is seeking an AI-Powered Technical Anchor to serve as a hands-on leader and technical authority for cross-functional product teams. This individual will partner closely with Forward Deployed Engineers (Java & React), work extensively within Google Cloud Platform (GCP), and leverage modern Generative AI technologies.

The ideal candidate will drive key architectural decisions, champion strong software craftsmanship practices—including Test-Driven Development (TDD) and pair programming and mentor fellow engineers.

They will also collaborate closely with Leadership, Product teams, Architects, and upstream/downstream application owners to deliver high-impact AI-powered products into production applications.

1. Technical Leadership & Strategy

• Be the Technical Authority: Design overall application architecture, code quality, and managing technical debt.
• Lead in a Balanced Team: Partner with Product Managers, Product Designers, and business leaders to evaluate feasibility, shape product roadmaps, and translate requirements into actionable user stories.
• Set the Engineering Standard: Raise the bar for software craftsmanship by driving practices like Test-Driven Development (TDD), Pair Programming, Continuous Integration/Continuous Delivery (CI/CD), and automated code reviews.
• Grow the Team: Coach, mentor, and level up engineers in clean code principles, GCP architecture, and AI-assisted development tools.

2. Full-Stack Application Architecture (Java & React)

• Architect Resilient Java Backends: Design and scale secure, high-concurrency microservices using Java 17+ (Spring Boot), gRPC, and REST APIs.
• Guide Interactive React Frontends: Lead frontend design using React, TypeScript, and modern state management to deliver smooth, real-time user experiencesmincluding streaming AI response UI and interactive dashboards.
• Standardize AI Tooling: Establish best practices for AI coding assistants (GitHub Copilot, Claude, Cursor, Gemini Code Assist) to boost velocity without compromising on code security or quality.

3. AI Architecture, RAG & LLMOps Integration

• Design Enterprise GenAI Systems: Architect scalable AI capabilities leveraging GCP Vertex AI, foundation models (Gemini, Claude, PaLM), and custom model fine-tuning.
• Build RAG Pipelines: Create high-efficiency semantic retrieval systems with GCP Vector Search, Pgvector, or dedicated vector databases, using advanced chunking, reranking, and context caching strategies.
• Enable Agentic Workflows: Construct multi-agent orchestrations, tool-execution harnesses, and function-calling workflows with frameworks like LangChain, LlamaIndex, or AutoGen.
• AI Observability & Guardrails: Build telemetry for response latency, cost tracking (token budget management), prompt injection protection, drift/hallucination detection, and fallback systems.

4. GCP Infrastructure & Cloud Operations

• Modern Cloud Infrastructure: Drive cloud-native designs using Cloud Run, Google Kubernetes Engine (GKE), Pub/Sub, BigQuery, and Terraform for Infrastructure-as-Code (IaC).
• Enterprise Security & Compliance: Enforce robust IAM policies, VPC networking security, data encryption, and AI compliance standards.
• DevOps Pipelines: Build and maintain automated CI/CD pipelines to ensure safe, continuous releases across all cloud environments.

Core Background

• 10+ years of professional full-stack software development experience, including at least 2–3 years as a Technical Lead, Software Architect, or Technical Anchor.
• Proven experience leading engineering teams through complex technical choices and delivering production software.

Technical Skill Set

• Java Mastery: Deep expertise in Java 17+ and frameworks like Spring Boot, Quarkus, or Micronaut. Strong grasp of event-driven design (Kafka, Pub/Sub), REST/gRPC, databases (PostgreSQL, Cloud Spanner/Bigtable), and TDD.
• React Mastery: Senior-level proficiency in React, TypeScript, state management (Redux Toolkit, Zustand, React Query), and real-time streaming interfaces (WebSockets, SSE).
• Google Cloud Expertise: Hands-on operational experience across core GCP tools (Cloud Run, GKE, IAM, Pub/Sub, Compute Engine) and Vertex AI.
• Applied GenAI Knowledge: Hands-on experience architecting RAG pipelines, vector indices, LLM orchestration frameworks, and prompt security/cost management.

Nice-to-Haves

• Certifications: GCP Professional Cloud Architect, GCP Professional ML/Data Engineer, or AIGP (AI Governance).
• Advanced AI & DevOps: Fine-tuning open-source models (Llama, Gemma), OWASP LLM Top 10 security framework, and hands-on container orchestration with Docker, Kubernetes, Helm, and Terraform.

What Success Looks Like in This Role (KPIs)

• Architectural Quality & Stability: High uptime, zero critical production outages, and low defect rates across both Java and React codebases.
• Delivery Velocity: High, predictable sprint velocity powered by automated testing, continuous integration, and AI-assisted workflows.
• AI System Accuracy & Cost Control: Low hallucination rates, optimized response times, and effective token budget management.
• Team Health & Craftsmanship: Strong developer satisfaction, high adoption of team practices (TDD and pairing), and minimal technical debt accumulation.