AI Engineer II

Deloitte’s HC Forward team is an innovation partner focused on modernizing Human Capital through AI, data, and engineering. In this role, you will design, build, and operate governed data, feature, and retrieval layers that power Human Capital AI and GenAI products, with a focus on production delivery, governance, observability, and cost or performance discipline.

What you’ll do

• Work with the Lead AI Solutions Architect and AI Data Engineer to translate Human Capital product needs into secure, scalable designs, including APIs, services, pipelines, and container or serverless solutions that meet availability, performance, and security expectations.
• Build and operationalize LLM-enabled capabilities such as copilots, HR knowledge assistants, summarization, and policy question-and-answer experiences, using Claude, GPT (Codex), and Gemini with secure endpoints, tool or function calling, and reusable prompt or context patterns.
• Implement core LLM application patterns, including retrieval-augmented generation (RAG), document ingestion and chunking, embeddings, vector or hybrid search, and retrieval evaluation telemetry.
• Deliver governed datasets and feature engineering or serving for ML training and real-time inference, with attention to online and offline consistency, caching, latency SLOs, and backfills.
• Operationalize data quality and lineage and support secure consumption for predictive ML and LLM-based experiences.

What you bring

• Ability to work independently and collaborate in a team environment.
• Effective written and verbal communication, with meticulous attention to detail.
• Capacity to lead projects or workstreams, manage priorities, meet deadlines, and provide clear guidance to others.
• Bachelor’s degree in a STEM field (example disciplines include Computer Science, Engineering, Statistics, or Data Science).
• 2+ years building and delivering LLM and GenAI solutions with Claude/GPT (Codex)/Gemini-class models, including prompt or context design, tool or function calling, evaluation, and production integration, plus 2+ years with RAG and retrieval including document processing, embeddings, vector or hybrid search, and enterprise governance controls.
• 2+ years of data and AI engineering, covering data modeling, batch or streaming pipelines, structured and unstructured processing, and feature engineering or serving fundamentals.
• 2+ years building production real-time inference services, including API design, latency and performance, and reliability patterns.
• 2+ years leading platform or integration engineering across enterprise systems, including REST, GraphQL, event-driven capabilities, microservices, and middleware; plus 2+ years DevOps and DevSecOps experience, including CI/CD, infrastructure as code with Terraform or CloudFormation, Docker or Kubernetes, and observability.
• 2+ years leading security and compliance efforts, including IAM, encryption, secrets, audit logging, and data privacy controls such as PII, retention, and access controls.
• Ability to travel 0–25% on average as needed based on work, clients, and industries or sectors served.
• Must be legally authorized to work in the United States without employer sponsorship.

Tools you’ll work with

Claude, GPT (Codex), Gemini, REST, GraphQL, Terraform, CloudFormation, Docker, Kubernetes, CI/CD, IaC, DevOps, DevSecOps

Team and benefits

Compensation: USD 91,100 - 179,500 per year.

Benefits: A discretionary annual incentive program, subject to program rules, where awards (if any) depend on factors including individual and organizational performance.

Preferred qualifications

• Master’s or doctoral degree and/or cloud and AI/ML certifications.
• 2+ years of Human Capital platform and integration experience, including Workday, SAP SuccessFactors, Oracle HCM, Salesforce, and HR data domains.
• 2+ years operationalizing LLMOps and MLOps capabilities, including evaluation, monitoring, governance workflows, and model or prompt or version management, plus 2+ years cloud experience with AWS, Microsoft Azure, or GCP, including managed data platforms and compute patterns.
• 2+ years translating business needs into documented requirements, acceptance criteria, and shippable increments using structured problem solving, and communicating AI/GenAI trade-offs involving quality, latency, cost, and risk.
• 2+ years collaborating across product, data science or machine learning, data engineering, platform, and security functions, and performing threat testing, monitoring, and operational-readiness responsibilities.
• 2+ years applying ethics and privacy practices around consent, PII, and bias boundaries, and experience with SOC 2, GDPR, or HIPAA.