AI Data Engineer - Senior Consultant
- 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.
- 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.
- 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 (online/offline consistency, caching, latency SLOs, backfills).
- Implement safety, privacy, and access controls (PII handling, prompt-injection defenses, content filtering, policy-based access) with security and risk stakeholders.
- Establish data/model reliability and cost-performance discipline (data quality, schema evolution, lineage/metadata, monitoring; right-sizing, query tuning, LLM token/cost telemetry).
- Contribute to MLOps/LLMOps and production operations (versioning, reproducibility, CI/CD, automated testing, observability, incident response); support design reviews, deployment readiness, and runbooks.
- 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); SOC 2/GDPR/HIPAA exposure a plus.
- Ability to travel 0-25%, on average, based on client and project needs.
- Limited immigration sponsorship may be available
- Advanced degree (MS/PhD) and/or relevant certifications (cloud and AI/ML).
- 4+ years of experience with Human Capital platforms and integrations (e.g., Workday, SAP SuccessFactors, Oracle HCM, Salesforce) and HR data domains.
- 4+ years of experience operationalizing LLMOps/MLOps capabilities (evaluation, monitoring, governance workflows, model/prompt/version management).
- 4+ years of cloud experience on AWS/Azure/GCP (one or more), including managed data platforms and scalable compute patterns.
- 4+ years of experience with structured problem solving, translating business needs into requirements, acceptance criteria, and shippable increments.
- 4+ years of experience with stakeholder communication: ability to explain AI/GenAI trade-offs (quality vs. latency vs. cost vs. risk) and document decisions.
- 4+ years of experience collaborating across product, data science/ML, data engineering, platform, and security.
- 4+ years of experience with treat testing, monitoring, and operational readiness as core responsibilities.
- 4+ years of experience with ethics and privacy awareness being able to recognize consent/PII/bias boundaries and escalate appropriately.
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