AI Engineer Senior Consultant

Deloitte Deloitte · Consulting

AI Engineer Senior Consultant role focused on building and operating data, features, and GenAI foundations for Human Capital AI products. Responsibilities include designing, building, and running the data/feature/retrieval layer, operationalizing LLM-enabled capabilities using models like Claude/GPT/Gemini, implementing RAG patterns, delivering governed datasets for ML training and inference, and establishing reliability, security, and cost-performance discipline. The role involves MLOps/LLMOps and production operations.

What you'd actually do

  1. Partner with the Lead AI Solutions Architect and AI Data Engineer to translate Human Capital product needs into secure, scalable technical designs and delivered solutions (APIs, services, pipelines, containers/serverless) meeting availability, performance, and security expectations.
  2. Build and operationalize LLM-enabled capabilities (e.g., copilots, HR knowledge assistants, summarization, policy Q&A) using Claude/GPT(Codex)/Gemini, including secure endpoints, tool/function calling, and reusable prompt/context patterns.
  3. Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation telemetry.
  4. Deliver governed datasets and feature engineering/serving for ML training and real-time inference (online/offline consistency, caching, latency SLOs, backfills).
  5. Implement safety, privacy, and access controls (PII handling, prompt-injection defenses, content filtering, policy-based access) with security and risk stakeholders.

Skills

Required

  • Bachelor’s degree in a STEM field (e.g., Computer Science, Engineering, Statistics, Data Science)
  • 4+ years building and delivering LLM/GenAI solutions with Claude/GPT(Codex)/Gemini-class models, including prompt/context design, tool/function calling, evaluation, and production integration.
  • 4+ years implementing RAG/retrieval (document processing, embeddings, vector/hybrid search) with enterprise governance controls.
  • 4+ years of modern data & AI engineering, including data modeling, batch/streaming pipelines, structured/unstructured processing, and feature engineering/serving fundamentals.
  • 4+ years building production, real-time inference services (API design, latency/performance, reliability patterns).
  • 4+ years leading platform/integration engineering across enterprise systems; strong API/integration experience (REST, GraphQL, event-driven, microservices, middleware).
  • 4+ years DevOps/DevSecOps experience (CI/CD, IaC such as Terraform/CloudFormation, Docker/Kubernetes, observability/monitoring).
  • 4+ years leading security/compliance efforts; familiarity with enterprise security controls (IAM, encryption, secrets, audit logging) and data/privacy (PII, retention, access controls)

Nice to have

  • SOC 2/GDPR/HIPAA exposure

What the JD emphasized

  • building and operating the data, features, and GenAI foundations
  • ship production pipelines and services
  • LLM applications using Claude-, GPT/Codex-, and Gemini-class models
  • strong governance, observability, and cost/performance discipline
  • trusted, governed data + feature + retrieval layer
  • secure, scalable technical designs and delivered solutions
  • LLM-enabled capabilities
  • secure endpoints, tool/function calling
  • reusable prompt/context patterns
  • LLM application patterns including RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation telemetry
  • governed datasets and feature engineering/serving for ML training and real-time inference
  • online/offline consistency, caching, latency SLOs, backfills
  • safety, privacy, and access controls
  • PII handling, prompt-injection defenses, content filtering, policy-based access
  • data/model reliability and cost-performance discipline
  • data quality, schema evolution, lineage/metadata, monitoring
  • right-sizing, query tuning, LLM token/cost telemetry
  • MLOps/LLMOps and production operations
  • versioning, reproducibility, CI/CD, automated testing, observability, incident response
  • design reviews, deployment readiness, and runbooks
  • building and delivering LLM/GenAI solutions with Claude/GPT(Codex)/Gemini-class models
  • prompt/context design, tool/function calling, evaluation, and production integration
  • implementing RAG/retrieval (document processing, embeddings, vector/hybrid search) with enterprise governance controls
  • modern data & AI engineering, including data modeling, batch/streaming pipelines, structured/unstructured processing, and feature engineering/serving fundamentals
  • building production, real-time inference services (API design, latency/performance, reliability patterns)
  • leading platform/integration engineering across enterprise systems; strong API/integration experience (REST, GraphQL, event-driven, microservices, middleware)
  • DevOps/DevSecOps experience (CI/CD, IaC such as Terraform/CloudFormation, Docker/Kubernetes, observability/monitoring)
  • leading security/compliance efforts; familiarity with enterprise security controls (IAM, encryption, secrets, audit logging) and data/privacy (PII, retention, access controls)

Other signals

  • building and operating data, features, and GenAI foundations
  • ship production pipelines and services that support model training, real-time inference, and LLM applications
  • design, build, and run the trusted, governed data + feature + retrieval layer used by AI/ML and GenAI solutions
  • Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation telemetry
  • Deliver governed datasets and feature engineering/serving for ML training and real-time inference
  • Implement safety, privacy, and access controls
  • Establish data/model reliability and cost-performance discipline
  • Contribute to MLOps/LLMOps and production operations