AI Engineer

Job Description

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.

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, counter parties, 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.

Qualifications

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

* Fluency in Python and production-grade services (microservices, REST/Graph

QL, 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 (One Lake, 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.

The Fine Print

* Must have unrestricted authorization to work in the United States.

* Must be willing to comply with pre-employment screening, including but not limited to drug testing, reference verification, and background check.

* Role may be hybrid/onsite at an Antares office; occasional travel as necessary.

#LI-CK1

#LI-hybrid

A reasonable estimate of the…