AI Platform Engineer

Job Title: Data Engineer / AI Engineer (Agentic AI Platform Financial Data) Location: Philadelphia, PA (Hybrid) Duration: 12+ months contract About the Role: We are building a platform that converts unstructured financial data (emails, corporate actions, index announcements) into high-quality, structured datasets used by financial institutions. This is not a typical "LLM wrapper" role. You will work on systems that: Extract data from noisy, inconsistent sources Validate and reconcile outputs across multiple inputs Ensure correctness, traceability, and auditability The challenge is not just applying LLMs-it's making them reliable in production for financial workflows. What You'll Work On Designing pipelines that process high-volume financial documents (batch + near real-time) Building LLM-powered extraction workflows (classification, parsing, summarization) Implementing validation layers (rule-based + model-based) to reduce hallucinations Developing retrieval systems using embeddings and vector search Architecting end-to-end systems: ingestion processing storage serving Ensuring data quality, observability, and fault tolerance Collaborating with product to turn messy data into usable financial intelligence Core Requirements Strong Python and backend/data engineering experience Experience building production data pipelines (ETL, streaming, or async systems) Solid understanding of distributed systems and failure modes Experience working with LLM-based systems in production: Prompt design, Output validation, Retry/fallback strategies, Evaluation and monitoring Experience with data storage systems (SQL + NoSQL) Familiarity with cloud infrastructure (AWS or similar) Preferred Experience Experience with RAG / vector search systems Background in financial data or capital markets Experience with streaming systems (Kafka, etc.) Experience building multi-step or agent-style workflows What Makes This Role Interesting Work on high-accuracy AI systems where correctness matters Solve real problems around: LLM reliability and hallucination mitigation Data consistency across conflicting sources Real-time vs correctness tradeoffs Build systems used in financial decision-making workflows High ownership over core architecture in an early-stage environment Nice to Know (but not required) Experience with orchestration tools (Airflow, etc.) Exposure to evaluation frameworks for LLMs Experience working with large-scale document processing Tech Stack (Representative, not exhaustive) Python, APIs, async processing LLM APIs + embeddings SQL / NoSQL databases Cloud infrastructure (AWS) Data pipelines and streaming systems Vector Databases For applications and inquiries, contact: hirings@openkyber.com