AI/ML 工程师
AI/ML Engineer
关于Luma Financial Technologies
Luma Financial Technologies(“Luma”)成立于2018年,开创了一款尖端的金融科技软件平台,已被全球的经纪/交易商公司、RIA办公室和私人银行采用。通过Luma,机构和个人投资者可以使用一个完全可定制的、独立的买方技术平台,帮助金融团队更高效地了解、研究、购买和管理另类投资以及年金。Luma为这些用户提供了全面的端到端流程生命周期管理能力,提供了一系列解决方案,包括教育资源和培训材料;定制结构化产品的创建和定价;电子订单输入;以及交易后管理。通过优先考虑透明度和易用性,Luma是一个多发行人、多批发商和多产品的选择,顾问可以利用它来最好地满足客户的特定投资组合需求。Luma总部位于俄亥俄州辛辛那提,同时在纽约、纽约、迈阿密、佛罗里达、苏黎世、瑞士和里斯本、葡萄牙设有办事处。如需更多信息,请访问Luma的网站。
关于该职位
我们正在寻找一位拥有至少3年AI/ML领域实际经验的优秀AI/ML工程师。这个令人兴奋的机会位于印度,向软件开发经理汇报,是你从零开始构建AI解决方案的机会,与我们行业最大的全球客户合作。理想的候选人应具备开发和部署先进机器学习模型和生成式AI(GenAI)解决方案的扎实背景。你将在将前沿的AI/ML功能——从GenAI、智能体、机器学习模型和提示技术到OCR、向量数据库和检索增强生成(RAG)——集成到我们的平台中发挥关键作用。具有AWS基础设施(包括AWS SageMaker和Bedrock)的经验是加分项。更重要的是,我们需要那些渴望实验、创新并交付有影响力的AI驱动解决方案的人。
你会做什么
- 领导金融科技中的AI创新:
- 设计、开发和部署推动下一代金融科技的先进AI/ML解决方案。
- 实现GenAI、基于智能体的系统和复杂的机器学习模型,以增强我们的平台能力。
- 负责完整的AI生命周期:
- 设计和实现支持AI/ML计划的稳健数据模型。
- 构建数据管道,确保数据的无缝流动
查看英文原文
About Luma Financial Technologies
Founded in 2018, Luma Financial Technologies (“Luma”) has pioneered a cutting-edge fintech software platform that has been adopted by broker/dealer firms, RIA offices, and private banks around the world. By using Luma, institutional and retail investors have a fully customizable, independent, buy-side technology platform that helps financial teams more efficiently learn about, research, purchase, and manage alternative investments as well as annuities. Luma gives these users the ability to oversee the full, end-to-end process lifecycle by offering a suite of solutions. These include education resources and training materials; creation and pricing of custom structured products; electronic order entry; and post-trade management. By prioritizing transparency and ease of use, Luma is a multi-issuer, multi-wholesaler, and multi-product option that advisors can utilize to best meet their clients’ specific portfolio needs. Headquartered in Cincinnati, OH, Luma also has offices in New York, NY, Miami, FL, Zurich, Switzerland and Lisbon, Portugal. For more information, please visit Luma’s website.
About the role
We are seeking a talented AI/ML Engineer with a minimum of 3 years of hands-on experience in the AI/ML domain. This exciting opportunity, based in India and reporting to the Software Development Manager, is your chance to build AI solutions from the ground up, working alongside the largest global clients in our industry.The ideal candidate will have a robust background in developing and deploying advanced machine learning models and generative AI (GenAI) solutions. You will play a pivotal role in integrating cutting-edge AI/ML capabilities—ranging from GenAI, Agents, ML Models, and Prompting Techniques to OCR, Vector Databases, and Retrieval Augmented Generation (RAG)—into our platform. Experience with AWS infrastructure, including AWS SageMaker and Bedrock, is considered a plus. More importantly, we’re looking for some who is eager to experiment, innovate, and delivery impactful AI-driven solutions.
What you'll do
- Lead AI Innovation in Fintech:
- Design, develop, and deploy advanced AI/ML solutions that power the next generation of financial technology.
- Implement GenAI, agent-based systems, and sophisticated ML models to enhance our platform capabilities.
- Own the Full AI Lifecycle:
- Design and implement robust data models that support AI/ML initiatives.
- Architect data pipelines to ensure seamless data integration and processing as part of the solution.
o Collaborate with cross-functional teams to integrate AI/ML functionalities into our multi-product, multi-issuer platform.
- Develop scalable machine learning pipelines and data processing workflows.
- Scale AI in the Cloud:
- Build, test, and optimize AI models on various cloud platforms; AWS experience (including SageMaker and Bedrock) is a bonus.
- Ensure robust deployment practices and maintain the performance and scalability of AI systems.
- Specialized Projects:
- Architect and implement an Agentic Framework tailored specifically for the needs of Financial Advisors, enabling autonomous reasoning, planning, and execution across complex financial [JP1] scenarios.
- Develop and enhance OCR capabilities and integrate these with vector databases.
- Utilize Retrieval Augmented Generation (RAG) techniques to improve data retrieval and decision-making processes.
- MLOps Integration (Plus):
- Champion MLOps best practices to streamline the continuous integration, delivery, and deployment of machine learning models.
- Collaborate with DevOps teams to optimize and monitor production-level AI/ML solutions.
- Be a Thought Leader:
- Provide technical guidance and mentorship to team members.
- Engage in knowledge-sharing sessions to drive continuous improvement across the team.
- Stay abreast of the latest advancements in AI/ML research, tools, and best practices.
- Experiment with novel prompting techniques and refine model architectures for improved outcomes.
Qualifications
- Experience:
- Minimum 3 years of professional experience in AI/ML engineering or a related field.
- Must have hands-on experience working on a commercial product that is already in production.
- Technical Expertise:
- In-depth knowledge of GenAI, agent-based systems, ML models, and prompting techniques.
- Practical experience with OCR technologies, vector databases, and Retrieval Augmented Generation (RAG).
- Proficient in programming languages such as Python and familiar with machine learning libraries (e.g., TensorFlow, PyTorch, scikit-learn).
- Cloud Experience:
- Experience with cloud platforms is beneficial; AWS experience (specifically with AWS SageMaker and Bedrock) is a plus but not required.
- Soft Skills:
- Strong problem-solving abilities.
- Excellent communication skills and a collaborative mindset.
- Ability to thrive in a fast-paced, innovative environment.
- Education:
- Advanced degree (Master’s or PhD) in Computer Science, Data Science, Machine Learning, or a related discipline.
- Financial Technology Exposure:
- Experience in the financial technology sector, particularly with structured products or annuities.
- Additional Technical Skills:
- Familiarity with containerization (Docker, Kubernetes) and CI/CD pipelines.
- Experience with DevOps best practices and contributing to open-source projects.
Originally posted on Himalayas