AI and Data Engineer

Fund or Department Description The Data & AI Engineer sits within Enterprise Technology & Data organization and supports firm-wide data and AI initiatives spanning investment platforms, portfolio operations, investor relations, and corporate functions. The role operates within a federated data operating model, partnering with domain engineering teams to implement shared platforms and reusable patterns for data and AI under the technical direction of the Senior AI & Data Architect. Position Summary The Data & AI Engineer is an experienced, hands‑on engineer who turns data and AI architecture into working production systems. Reporting to the Senior AI & Data Architect, this role is responsible for building and operating the pipelines, semantic layers, retrieval systems, and AI‑ready data products that power analytics, automation, LLMs, agents, and generative AI applications across the firm. The role requires deep, hands‑on expertise across modern data engineering and applied AI engineering. The Data & AI Engineer will implement retrieval‑augmented generation (RAG) patterns, embedding and indexing pipelines, vector stores, and semantic models alongside core ELT, streaming, and analytical pipelines - treating LLMs, agents, and copilots as first‑class consumers of the data platform. This is a senior individual‑contributor engineering role that executes against architectural standards, contributes to their evolution through hands‑on learning, and partners closely with data science, AI engineering, governance, and domain teams to deliver trusted, AI‑consumable data at enterprise scale. What Success Looks Like In the first 12 months, this role will deliver foundational AI‑ready data pipelines and retrieval components defined in the target‑state architecture, productionize one or more priority RAG or agent‑grounding use cases, and establish reusable engineering patterns that other domain teams can adopt across the federated data platform. In‑office requirement: 4 days per week Primary Responsibilities AI Data Pipelines & Retrieval Systems Build and operate AI‑ready data pipelines - embedding generation, chunking, indexing, and refresh workflows - that make enterprise data reliably retrievable by LLMs, agents, and generative AI applications. Implement retrieval‑augmented generation (RAG) components, including vector store integrations, hybrid search, re‑ranking, and grounding logic, against architectural patterns defined by the Senior AI & Data Architect. Develop and maintain tool and function interfaces that allow agents and copilots to query and act on enterprise data safely, with appropriate guardrails, logging, and evaluation hooks. Partner with Data Science and AI Engineering teams to operationalize feature stores, evaluation datasets, and reusable AI data products. Contribute to semantic and context engineering work that powers natural‑language analytics, conversational reporting, and AI‑driven insights for business users. Modern Data Pipeline Engineering Design, build, and maintain production‑grade ELT, streaming, and transformation pipelines using tools such as dbt, Fivetran and Snowflake. Implement ingestion, modeling, and consumption patterns that meet enterprise standards for scalability, performance, security, resiliency, and cost efficiency. Write clean, well‑tested Python and SQL; apply software engineering best practices including version control, code review, CI/CD, modular design, and automated testing. Productionize new sources and domains under the federated operating model, partnering with domain data engineers to apply shared platform capabilities consistently. Semantic Layer & Data Product Development Implement semantic models, data contracts, and analytical/dimensional models that enable trusted self‑service analytics and reliable AI grounding. Build and maintain reusable data products with clear ownership, documented contracts, and contextual metadata suitable for both human and AI consumers. Collaborate with the Senior AI & Data Architect to refine and extend enterprise semantic standards based on what works in production. Support discovery and consumption tooling so that analysts, applications, and agents can find and use data products with minimal friction. Data Quality, Observability & AI Trust Implement data quality checks, lineage capture, and pipeline observability across both data and AI workloads. Build logging, evaluation, and monitoring components for AI systems - including prompt and response capture, retrieval metrics, and model performance signals - in line with governance standards. Partner with Data Governance to operationalize metadata, stewardship, and access controls, ensuring AI systems consume enterprise data with the same rigor as human users. Surface issues early, propose remediations, and feed lessons learned back into architectural patterns. Collaboration & Engineering Craft Participate in architectural design reviews and contribute hands‑on engineering perspective to evolving patterns and standards. Mentor junior data engineers and analysts on modern data and AI engineering practices. Document patterns, write runbooks, and share knowledge across the federated organization to accelerate adoption of reusable platform capabilities. Requirements Education & Certifications Bachelor's degree, required Concentration in computer science, data engineering, information systems, or a related field, preferred Masters degree, preferred Relevant certifications in cloud, data engineering, analytics, or AI/ML are preferred Professional Experience 6+ years of overall relevant technical experience, required Experience in data engineering, analytics engineering, or platform engineering, with at least 1-2 years of direct, hands‑on experience building generative AI or AI/ML systems in production. Proven experience implementing retrieval, grounding, and semantic components for LLM- or agent‑based applications, including RAG pipelines, vector stores, embedding workflows, and structured tool use. Hands‑on experience with one or more modern AI platforms and tooling categories (e.g., AWS Bedrock, Databricks ML, Snowflake Cortex, OpenAI/Anthropic APIs, LangChain/LlamaIndex or equivalents, MLflow, and vector databases such as Databricks Vector Search, pgvector, or Pinecone). Strong, demonstrable expertise in Python and SQL, with working knowledge of distributed processing frameworks (e.g., Spark). Deep, hands‑on experience with modern data stacks - dbt, Fivetran, Snowflake - in AWS‑based environments. Track record of building data pipelines and products whose consumers include AI systems, not only BI tools and human analysts. Palantir experience a plus. Experience operating within federated data operating models and complex, regulated enterprise environments; financial services experience preferred. Competencies & Attributes Demonstrated AI‑forward instinct: defaults to asking how AI changes what gets built, rather than whether AI can be added later. Fluency in current AI engineering patterns (RAG, agents, tool use, evaluations, guardrails, observability) and the practical trade‑offs involved in shipping them. Strong engineering craft: clean code, automated testing, thoughtful design, and a bias toward production‑quality systems over prototypes. Pragmatic, delivery‑oriented mindset with strong attention to data quality, AI trust, and long‑term maintainability; able to distinguish durable engineering decisions from AI hype. Collaborative partner to architects, data scientists, AI engineers, and domain teams; comfortable operating in a matrixed, federated organization and in high‑visibility transformational initiatives. Dexian is an Equal Opportunity Employer that recruits and hires qualified candidates without regard to race, religion, sex, sexual orientation, gender identity, age, national origin, ancestry, citizenship, disability, or veteran status. #J-18808-Ljbffr