***No C2C, this role is FTE only and is unable to consider sponsorship at this time***
Location:
Our client is targeting someone who can do hybrid in Charlotte, NC
About the Company:
Our client is a leading organization in the financial services space, partnering with professionals across the United States to deliver a broad range of financial products and services. The company focuses on expanding access through strong product offerings, innovative marketing, and advanced technology.
About the Team:
The AI & Analytics team is focused on building next-generation intelligent capabilities across the organization. The team combines modern machine learning, data engineering, and automation to enhance operations, improve decision-making, and support business growth. This is a fast-paced, collaborative, and experimentation-driven environment partnering cross-functionally with technology, operations, and business leaders. The team develops AI solutions including predictive models, enterprise automation, retrieval-augmented systems, and generative AI applications while maintaining strong governance, transparency, and responsible AI practices.
Position Overview:
The Senior Machine Learning / AI Engineer will play a key role in designing, building, and deploying scalable AI systems. This individual will develop production-grade machine learning models, generative AI applications, and automated workflows that drive efficiency and business impact.
Key Responsibilities:
Design, build, and deploy machine learning models and AI systems at scale
Develop LLM-powered applications including RAG pipelines, agent workflows, and decision support tools
Partner with cross-functional teams to translate business needs into technical solutions
Collaborate with data engineering to ensure data quality and availability
Mentor team members on best practices in MLOps and model development
Implement monitoring, observability, and governance frameworks
Maintain standards for security, compliance, and responsible AI usage
Evaluate new tools, models, and frameworks to enhance capabilities
Present insights and technical solutions to both technical and non-technical stakeholders
Qualifications:
Strong proficiency in Python and modern machine learning frameworks such as PyTorch, TensorFlow, or scikit-learn
Experience building and deploying machine learning models in production environments
Hands-on experience with large language models, transformers, embeddings, vector databases, and retrieval-augmented generation
Experience designing end-to-end AI pipelines from data ingestion through deployment and monitoring
Familiarity with prompt engineering, fine-tuning, and model optimization techniques
Experience with MLOps tools such as MLflow, Databricks, Weights & Biases, or SageMaker
Knowledge of agent frameworks such as LangChain, Semantic Kernel, CrewAI, or AutoGen
Experience with cloud platforms, preferably AWS, as well as containerization and orchestration
Strong SQL skills and experience working with modern data platforms such as Snowflake, Redshift, or BigQuery
Experience building APIs, microservices, and distributed systems
Ability to evaluate model performance and optimize for accuracy, fairness, and reliability
Strong critical thinking and problem-solving skills
Ability to translate business requirements into scalable technical solutions
Strong communication skills with the ability to explain complex concepts clearly
Experience working cross-functionally with engineering, data, and business teams
Ability to mentor and influence team members and stakeholders
Preferred Qualifications:
Experience in financial services, insurance, or other regulated industries
Exposure to AI governance and responsible AI frameworks
Experience with data governance or model lineage practices
Familiarity with experimentation frameworks or A/B testing
Knowledge of reinforcement learning, recommendation systems, or advanced NLP techniques