Generative AI Engineer

AI/ML Engineer – Financial ServicesAbout All In On Data NO SPONSORSHIP AVAILABLE FOR THIS ROLE NO CORP to CORP (C2C) At All In On Data, we specialize in leveraging cutting-edge data analytics and artificial intelligence to deliver transformative solutions for the financial services industry. Our mission is to empower businesses with actionable insights, enabling them to make smarter, data-driven decisions. As an industry leader, we are committed to fostering innovation, promoting collaboration, and driving value for our clients through advanced AI and machine learning capabilities. If you're passionate about solving complex problems in fintech and want to make a meaningful impact, we want you on our team. The Role We are seeking a talented and driven AI/ML Engineer with strong engineering and machine learning expertise to advance our technical capabilities in Artificial Intelligence and Machine Learning. In this role, you will work on high-impact AI/ML projects across multiple business units, developing cutting-edge solutions that enhance efficiency, reveal insights, and drive value in the financial sector. You will have the opportunity to lead hands-on development efforts, mentor team members, and contribute to best practices for AI/ML development and deployment. This role will focus heavily on leveraging Microsoft Azure cloud services to build scalable AI solutions. As part of the All In On Data team, you will collaborate with cross-functional groups, including engineering, product, data science, and business stakeholders, to deliver innovative AI-driven solutions that align with our mission of excellence in financial services. What You’ll Do • Design, develop, and deploy scalable and robust AI/ML solutions to solve critical challenges in the financial services industry. • Engage in hands-on development using Azure Machine Learning (Azure ML), Azure OpenAI Service, and other Azure AI tools to create impactful solutions. • Implement Retrieval Augmented Generation (RAG) techniques to enhance large language model (LLM) capabilities for financial use cases. • Lead efforts to fine-tune pre-trained models (e.g., LLMs) for proprietary financial datasets and specific business problems. • Work with data engineering and platform teams to develop and maintain data pipelines and MLOps workflows within the Azure ecosystem. • Define the technical direction and architecture for AI/ML projects while promoting best practices and maintaining high standards for solutions built on Azure. • Advocate for and implement best practices in model development, validation, deployment, monitoring, and continuous improvement of AI systems. • Stay current with AI/ML advancements, Azure AI services, and financial technology (FinTech) trends, and apply this knowledge to deliver cutting-edge solutions. • Collaborate across teams to integrate AI/ML capabilities into both new and existing financial products, systems, and processes. • Provide technical mentorship to engineers and data scientists to foster a culture of innovation and data-driven decision-making. • Communicate complex AI/ML concepts to both technical and non-technical audiences in a clear and effective manner. What You’ll Need • Education: Bachelor’s or Master’s in Computer Science, Engineering, Statistics, Artificial Intelligence, or a related quantitative field. • Experience: • Proven industry experience (5+ years, flexible based on capability) in designing, building, and deploying machine learning/AI models in production environments, ideally within financial services or regulated industries. • Deep hands-on experience with Microsoft Azure cloud services for AI/ML, including Azure ML Studio, Azure Blob Storage, Azure Functions, Azure Cognitive Services, and Azure Databricks. • Expertise in Retrieval Augmented Generation (RAG) methodologies and fine-tuning large language models (LLMs) or other foundation models for specific datasets and use cases. • Strong programming skills in Python and hands-on experience with common ML libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch, Hugging Face). • Solid understanding of MLOps principles and hands-on experience with model deployment, monitoring, and lifecycle management within the Azure ecosystem. • Familiarity with data engineering tools (e.g., Spark), version control systems (e.g., Git), and containerization technologies (e.g., Docker, Kubernetes). • Skills: • Excellent analytical, problem-solving, and critical thinking abilities. • Strong verbal and written communication skills, with the ability to effectively collaborate with diverse teams. • A strong aptitude for quickly learning financial concepts and regulatory requirements (experience in financial services is highly desirable). Who You Are • Motivated & Resourceful: You find solutions even when faced with challenges and take ownership of your work. • Collaborative Team Player: You thrive in a collaborative environment and contribute positively to your team’s success. • Passionate Innovator: You are deeply motivated to build creative and robust solutions for complex problems. • Detail-Oriented & Quality-Focused: You take pride in delivering high-quality work while remaining pragmatic and focused on impact. • Customer-Obsessed: You are dedicated to solving problems that deliver real value to businesses and clients. • Growth-Oriented: You are eager to learn new technologies, improve your skills, and grow within the fintech space. • Mentorship-Driven: You enjoy mentoring others and fostering technical growth within your team. Why Join All In On Data? At All In On Data, we are committed to building an inclusive and innovative workplace where your contributions have a direct impact on the financial services industry. You’ll work alongside a team of talented professionals, tackle exciting challenges, and have the opportunity to grow your career in a cutting-edge environment. Join us and be part of shaping the future of AI in fintech.