Senior Machine Learning Engineer

WHO WE ARE: Financial Independence Group (FIG) is one of the nation’s largest Finance and Insurance Marketing Organizations (FMO & IMO) in the country, partnering with thousands of financial professionals in all 50 states. FIG seeks to expand the availability of financial products and services to financial professionals and ultimately the clients and families they serve. This is accomplished through robust product offerings, innovative marketing, and cutting-edge technology. Interested in learning more? Find out what it’s like to work at FIG. ABOUT THE TEAM: FIG’s AI & Analytics Team is advancing the next generation of intelligent capabilities across the organization. We combine state‑of‑the‑art machine learning, modern data engineering, and agent‑driven automation to enhance how FIG supports advisors, strengthens operations, and accelerates strategic decision‑making. We operate in a fast‑paced, collaborative, and experimentation‑friendly environment. Our team partners cross‑functionally with technology, operations, and business leaders to develop AI systems that deliver measurable impact, ranging from predictive models and enterprise automations to retrieval‑augmented assistants and large‑scale generative AI solutions. Innovation, iteration, and continuous improvement are core to how we work. We build, test, refine, and deploy AI capabilities that push boundaries and elevate FIG’s intelligence ecosystem. As we scale the use of AI across the business, we also uphold strong governance, responsible‑use standards, and transparency to ensure our systems remain safe, ethical, and high‑trust. YOUR ROLE: As a Senior Machine Learning/AI Engineer , you play a critical role in architecting, building, and operationalizing FIG’s next generation of intelligent systems. You will develop production‑grade machine learning models, generative AI capabilities, and automated workflows that support advisors, enhance internal operations, and drive organizational efficiency. You will: Design, build, and deploy machine learning models and AI systems at scale. Develop LLM‑powered applications, including RAG pipelines, agent workflows, and automated decision‑support tools. Collaborate with cross‑functional partners to translate business needs into technical AI solutions. Work closely with data engineering to ensure strong data availability, quality, and ML readiness. Mentor engineers and analysts, guiding best practices in MLOps, model development, and responsible AI. Implement monitoring, observability, and model governance frameworks. Maintain high standards for security, safety, and compliance aligned with FIG’s AI governance. Evaluate new models, tools, and frameworks to continuously expand FIG’s AI capabilities. Present recommendations, insights, and solution designs to technical and non‑technical stakeholders. WHY YOU ARE THE RIGHT PERSON: You thrive in a fast‑paced, highly collaborative environment. You are a builder at heart—creative, curious, and excited by solving complex technical problems. You blend strong engineering fundamentals with practical ML/AI expertise. You understand how to design models that solve real business problems and scale effectively. You are passionate about mentorship and leveling up the technical skillsets around you. You communicate clearly and can explain complex concepts to non‑technical audiences. You bring a forward‑thinking mindset, constantly seeking opportunities to innovate and automate. You embrace iteration and continuous improvement in everything you build. You believe in responsible AI and understand the importance of governance, safety, and human oversight. WHAT YOU BRING: Technical Skills Strong proficiency in Python and modern ML frameworks (PyTorch, TensorFlow, scikit‑learn). Experience building and deploying machine learning models into production environments. Hands‑on experience working with large language models (LLMs), transformers, vector databases, embeddings, and retrieval‑augmented generation (RAG). Ability to design end‑to‑end AI pipelines from data ingestion and feature engineering to training, evaluation, deployment, and monitoring. Familiarity with prompt engineering, fine‑tuning, and model optimization techniques. Experience implementing MLOps workflows and tools (MLflow, Databricks, Weights & Biases, SageMaker, AgentCore or similar). Knowledge of agent frameworks (Strands, LangChain, Semantic Kernel, CrewAI, AutoGen). Knowledge of cloud technologies (AWS preferred), containerization and orchestration. Strong SQL skills and comfort working with modern data platforms (e.g., Snowflake, Redshift, BigQuery, Synapse). Experience deploying APIs, microservices, and distributed systems. Analytical Skills Ability to evaluate model performance, identify failure modes, and optimize for accuracy, fairness, and reliability. Strong critical‑thinking skills paired with creativity in designing AI‑driven solutions. Experience conducting exploratory analysis, model experimentation, and error analysis. Ability to balance technical excellence with practical business impact. Business & Communication Skills Ability to translate business needs into scalable AI solutions. Experience partnering with cross‑functional teams across engineering, data, operations, and leadership. Strong communication skills with the ability to present complex concepts clearly. Ability to mentor, guide, and influence teammates and stakeholders. Bonus Experience (Nice to Have) Experience in financial services, insurance, wealth management, or other regulated industries. Prior involvement in AI governance, responsible AI frameworks, or risk assessments. Experience contributing to data governance, metadata management, or model lineage documentation. Familiarity with experimentation frameworks, A/B testing, or statistical modeling. Working knowledge of reinforcement learning, recommendation systems, or advanced NLP techniques. #J-18808-Ljbffr