AI Engineer

Antares Capital•Chicago, IL

3d•Hybrid

About The Position

Antares Capital is seeking an AI Engineer to join our Data & Analytics Technology team. In this hands-on role, you will design, build, and operate production-grade AI capabilities that power decision-making across the firm—with a focus on Retrieval-Augmented Generation (RAG), vector database–backed retrieval, and the orchestration of multiple Large Language Models (LLMs). You will help shape our AI architecture to be agile, flexible, and built to last—emphasizing modularity, reliability, and secure-by-design practices appropriate for financial services. The ideal candidate brings 3+ years of experience delivering AI/ML solutions (including 2+ years with LLM-based systems), a strong engineering and architecture mindset, and a passion for responsible innovation in a regulated environment.

Requirements

5+ years of industry experience building and deploying AI/ML applications, including 2+ years with LLM-based systems (preferably in financial services).
Hands-on expertise with RAG: embedding generation, retrievers, prompt construction, context management, and hallucination mitigation.
Deep understanding of vector databases and embedding frameworks; ability to tune similarity search (cosine, dot-product) and index parameters.
Proven experience with ontology-driven data modeling (business entities, taxonomies, knowledge graphs, semantic modeling) and mapping from physical schemas to conceptual models.
Fluency in Python and production-grade services (microservices, REST/GraphQL, event-driven patterns); strong software engineering fundamentals.
Proficiency with big data and machine learning platforms such as Databricks (Spark, Delta Lake, Unity Catalog) and experience operating at scale.
Experience with large-scale cloud data/AI solutions, including Microsoft Fabric (OneLake, Lakehouse, semantic models, pipelines) or equivalent enterprise data/AI fabric, and common cloud services (Azure preferred).
Grounding LLMs with curated, versioned knowledge sources; experience with data pipelines and ETL/ELT concepts.
Strong grasp of evaluation, observability, and MLOps for LLMs (dataset management, A/B testing, drift/quality monitoring, prompt/version governance).
Practical experience with CI/CD, Docker/containers, and infrastructure-as-code (Terraform or equivalent).
Awareness of financial-industry considerations: data privacy, model risk/governance, auditability, and secure development practices.
Excellent communication skills and the ability to influence and collaborate across product, platform, data, and risk/security teams.

Nice To Haves

Any experience with 3rd party platform (eg: Palantir/Foundry) implementations is a plus.

Responsibilities

Design and implement robust RAG pipelines integrating domain datasets, embeddings, and retrieval strategies to deliver accurate, auditable responses.
Lead the evaluation and integration of vector databases (e.g., FAISS, Pinecone, Milvus) and tune indexing/embedding strategies for performance and relevance.
Architect and orchestrate combinations of LLMs and tools (routing, ensemble prompts, function-calling, guardrails) to optimize quality, latency, and cost.
Drive an ontology-driven approach: model and map enterprise data to real-world business concepts (e.g., customers, counterparties, facilities, equipment) rather than siloed technical tables; steward canonical vocabularies, taxonomies, and knowledge graphs.
Partner with data and platform teams to establish and evolve a semantic layer that aligns data products with business entities, definitions, and policies; ensure traceability from ontology to physical data stores.
Contribute to and extend the AI reference architecture emphasizing modular services, clear interfaces, observability, and change-tolerant design.
Develop secure data access patterns (role-based permissions, PII minimization) and implement content filtering, redaction, and safety controls.
Build evaluation frameworks (automated tests, offline/online metrics, human-in-the-loop review) and maintain datasets for regression benchmarking.
Implement CI/CD and containerization for AI services; instrument telemetry, tracing, and feature flags for safe progressive delivery.
Collaborate with product, data, risk, and security teams to translate business needs into pragmatic AI solutions aligned to industry compliance and model risk management.
Troubleshoot production issues, conduct post-incident reviews, and drive reliability improvements (SLOs, error budgets, resilience testing).
Mentor engineers, review designs/code, and champion engineering excellence and documentation across the AI platform.