AI/ML Engineer
$130k - $160kLaStellar Group
Job Description
AI/ML Engineer — Java Backend + Applied ML
\nA 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.
\nThe role
\nThis 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.
\nWhat you'll actually build
\n- \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
What we need
\n- \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
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.
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