About Careertakes

👉 Important disclosure: Careertakes is a third-party recruiting platform supporting this hiring process. If selected, you will be employed directly by our client, Financial Services.

Applicants for this role may also receive access to additional matched opportunities through the Careertakes platform.

---

Role overview

Confidential Client is hiring an AI/ML Software Engineer to build production-grade machine learning systems used in mission-critical financial workflows. You’ll work on back-end services, model integration, MLOps, and agentic AI patterns, partnering with product, data, and platform teams. This is a New York City–based role (5 Penn Plaza) with a hybrid schedule: four days in-office (Mon–Thu) and one remote day (Fri).

---

What You’ll Do

• Design, build, and maintain production backend services that power AI/ML capabilities using Java and Spring Boot.
• Integrate and serve trained ML models for inference; develop training and evaluation pipelines in Python.
• Implement event-driven data flows and streaming integrations using Apache Kafka.
• Build and maintain data pipelines, feature stores, and ETL workflows to support model training and inference.
• Architect and implement Agentic AI and multi-agent workflows, including orchestration and tool-use patterns.
• Implement MLOps best practices: model versioning, monitoring, A/B testing, automated retraining, and model governance.
• Ensure models are reliable, explainable, and performant in real-world financial environments.
• Participate in code reviews, write tests, and document systems and ML components.

---

Minimum qualifications

• 2+ years of professional software engineering experience with strong proficiency in Java and Spring Boot.
• 2+ years of hands-on AI/ML development experience in Python.
• Experience building event-driven systems with Apache Kafka.
• Solid understanding of relational databases (PostgreSQL) and data warehousing (Redshift or equivalent).
• Hands-on experience with Redis for caching or queuing.
• Familiarity with ML frameworks such as scikit-learn, PyTorch, TensorFlow, or XGBoost.
• Working knowledge of ML concepts: feature engineering, model evaluation, hyperparameter tuning.
• Understanding of LLMs, RAG architectures, and prompt engineering concepts.
• Strong software engineering fundamentals: REST API design, microservices, distributed systems, and testing.

---

Preferred / Nice-to-have

• Experience with agent orchestration frameworks or libraries (e.g., LangChain, LangGraph, Spring AI).
• Production MLOps experience (model serving, monitoring, rollout strategies).
• Background in financial services or workflows that handle sensitive, high-value data.
• Experience with Redshift and large-scale data warehousing.
• Familiarity with Model Context Protocol (MCP) or similar orchestration patterns.

---

Compensation & benefits

• Base pay range (New York): $130,000 - $160,000 per year. Pay may vary based on experience and qualifications.
• Equity and year-end bonus may be offered depending on role and level.
• Comprehensive benefits package including company-paid healthcare, flexible PTO, parental leave, daily office amenities, and more.

---

Why join

• High-impact work: build infrastructure and AI systems that modernize legacy, high-value markets.
• Cross-functional teams: close collaboration with product, data science, and platform engineering.
• Modern tech stack and strong emphasis on engineering best practices and responsible AI.

---

Location & schedule

• Office: New York City — 5 Penn Plaza (hybrid: Mon–Thu in-office; Fri remote).
• This role requires work authorization to be employed in the United States.

---

Equal Opportunity & Hiring Transparency

Careertakes and our client are Equal Opportunity Employers committed to building a diverse and inclusive workforce. We prohibit discrimination or harassment of any kind. To support a fair and efficient hiring process, AI tools may be used to assist with application review or resume screening. These tools do not replace human decision-making. Final hiring decisions are made by people.

If you have questions about how your data is used, please contact us directly.