AI Engineer Consultant

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Company Overview

A leading global professional services organization is seeking an AI Engineer Consultant to help build and operate the data, feature engineering, and Generative AI foundations that power Human Capital AI products and analytics. This role focuses on delivering secure, scalable, and production-ready AI solutions by collaborating with cross-functional engineering, product, security, and data science teams. The successful candidate will contribute to the development of enterprise-grade AI platforms, enabling advanced analytics, machine learning, and large language model (LLM) applications.

Key Responsibilities

• Design, build, and maintain trusted data, feature, and retrieval layers supporting AI/ML and Generative AI solutions.
• Develop reproducible datasets and feature pipelines while ensuring data quality, governance, and lineage.
• Partner with AI architects and data engineers to translate business requirements into secure, scalable technical solutions.
• Build and operationalize LLM-powered applications such as AI copilots, knowledge assistants, summarization tools, and policy question-answering systems.
• Implement Retrieval-Augmented Generation (RAG) architectures, including document ingestion, chunking, embeddings, vector databases, hybrid search, and retrieval evaluation.
• Develop production-ready APIs, services, pipelines, and cloud-native applications supporting model training and real-time inference.
• Deliver feature engineering and serving capabilities for machine learning training and online inference workloads.
• Ensure AI solutions meet performance, availability, security, governance, and compliance standards.
• Collaborate closely with product, platform engineering, security, and data science teams throughout the software development lifecycle.
• Apply DevOps and MLOps practices to improve deployment automation, monitoring, observability, and operational readiness.

Required Qualifications

• Bachelor’s degree in Computer Science, Engineering, Statistics, Data Science, or another STEM-related field.
• 2+ years of experience building and deploying Generative AI solutions using enterprise-grade large language models.
• 2+ years of experience implementing Retrieval-Augmented Generation (RAG), document processing, embeddings, and vector search solutions.
• 2+ years of experience in modern data engineering, including batch and streaming pipelines, structured and unstructured data processing, and feature engineering.
• 2+ years of experience building production-grade, real-time inference services and APIs.
• 2+ years of experience designing and integrating enterprise systems using REST APIs, GraphQL, microservices, or event-driven architectures.
• 2+ years of DevOps and DevSecOps experience, including CI/CD pipelines, Infrastructure as Code, containerization, Kubernetes, and monitoring.
• Experience implementing enterprise security controls, identity management, encryption, audit logging, and privacy requirements.
• Strong written and verbal communication skills.
• Excellent analytical, organizational, and problem-solving abilities.
• Ability to manage multiple priorities in a fast-paced environment.
• Ability to travel up to 25% based on project requirements.
• Must be legally authorized to work in the United States without current or future employer sponsorship.

Preferred Qualifications

• Master’s or PhD in a relevant technical field.
• Professional cloud or AI/ML certifications.
• Experience with Human Capital platforms such as Workday, SAP SuccessFactors, Oracle HCM, or Salesforce.
• Experience implementing MLOps and LLMOps capabilities, including model evaluation, governance, monitoring, and version management.
• Experience with AWS, Microsoft Azure, or Google Cloud Platform.
• Strong understanding of translating business requirements into technical solutions and product deliverables.
• Experience communicating AI trade-offs involving quality, latency, cost, and risk to technical and non-technical stakeholders.
• Experience collaborating across product management, data science, engineering, platform, and security teams.
• Knowledge of AI ethics, privacy, consent management, and responsible AI practices.

Disclaimer

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1. No Endorsement: Job ads on SpringCube do not imply endorsement of their authenticity or quality.
2. No Client Relationship: This company is not a client of SpringCube unless stated.
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