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AI/ML Engineer

$130k - $160k

LaStellar Group

Job Description

AI/ML Engineer — Java Backend + Applied ML

\n

A well-funded fintech is building the infrastructure that loans and CLO markets have never had. Trillions of dollars still move through phones, emails, and PDFs. This team is applying AI, real-time data, and modern software design to a market that's never had it — and needs an engineer who can build production ML systems, not just prototype them.

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The role

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This is a Java backend engineer who also owns the ML side — you're not handing models off to someone else to productionize, you're building the services, the pipelines, and the models yourself. You'll take AI solutions from concept through production in a real financial environment where reliability and explainability aren't optional. NYC-based (Manhattan), 4 days in-office, Friday remote.

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What you'll actually build

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    \n
  • Production backend services powering AI/ML capabilities — Java, Spring Boot, Kafka, Redis, PostgreSQL, Redshift
  • \n
  • ML models trained, evaluated, and deployed using Python frameworks (scikit-learn, PyTorch, TensorFlow)
  • \n
  • Agentic AI systems — multi-agent workflows, tool-use pipelines, orchestration frameworks (MCP or similar)
  • \n
  • Data pipelines and feature engineering workflows supporting training and inference
  • \n
  • MLOps infrastructure — model versioning, monitoring, A/B testing, automated retraining
  • \n
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What we need

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    \n
  • 2+ years professional software engineering with strong Java and Spring Boot
  • \n
  • Experience with event-driven systems (Kafka), PostgreSQL, Redshift or equivalent data warehousing, Redis
  • \n
  • Solid grasp of distributed systems, REST API design, microservices
  • \n
  • 2+ years hands-on AI/ML development in Python — real model-building experience, not just API calls to a hosted model
  • \n
  • Working knowledge of ML fundamentals: supervised/unsupervised learning, model evaluation, feature engineering, hyperparameter tuning
  • \n
  • Experience with scikit-learn, PyTorch, TensorFlow, or XGBoost
  • \n
  • Understanding of LLMs and GenAI — prompt engineering, fine-tuning, RAG
  • \n
  • Bonus: exposure to agentic patterns — tool use, planning, multi-step reasoning, or frameworks like LangChain, LangGraph, CrewAI, Spring AI
  • \n
\n

Comp

\n

$130,000–$160,000 base, plus equity and year-end bonus. Full medical/financial benefits, 90% company-paid healthcare, flexible PTO, daily breakfast/coffee/snacks, onsite gym.

Vacancy posted 6 days ago
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