Senior Software Engineer - Hybrid

Our Fortune 500 company is driving a digital transformation and looking for forward-thinking innovators to disrupt how our industry thinks about and uses technology. As one of the world's leading employee benefits providers, we help millions of people gain affordable access to benefits that help them protect their families, their finances and their futures.

Are you an asker of questions, a solver of problems, and a challenger of the status quo? Our mission is to provide a differentiated customer experience and exceed the expectations people have of technology at any company — not just insurers. 

We are seeking individuals to join our team of talented IT professionals who share never-ending passion and an unwavering focus on our customer experience. Team members comfortable working in an agile, fast-paced, and delivery-focused environment thrive in our environment where we value an entrepreneurial spirit and those who challenge the status-quo.

Unum is changing, and we’re excited about what’s next. Join us.

General Summary:

As a Senior Software Engineer within the Digital Incubator & Applied AI team, you will play a pivotal role in developing and deploying cutting-edge AI solutions, bridging the gap between experimental concepts and scalable production models. This position offers the opportunity to apply advanced engineering skills in a fast-paced, Agile environment, focusing on rapid prototyping, collaboration, and technical leadership. If you’re driven by innovation and passionate about advancing AI technology, this role is designed for you.

*** Location: 2 days onsite per week at our Portland, ME, Chattanooga, TN, Columbia. SC or Atlanta, GA campus ***

Technical Requirements:

• Programming Languages: Proficient in Python, .NET, and C#. A solid understanding of .NET fundamentals—including concepts like dependency injection, value vs. reference types, and memory management (stack vs. heap)—is essential.
• AI/ML Deployment: Experienced in deploying machine learning models on cloud platforms (e.g., AWS SageMaker, Google AI Platform, Azure ML).
• Data Engineering: Skilled in data processing frameworks (e.g., Apache Spark, Kafka) and knowledgeable in SQL and NoSQL databases.
• Model Training & Evaluation: Strong understanding of supervised, unsupervised, and reinforcement learning models, with knowledge of relevant evaluation metrics.
• API & Microservices: Expertise in creating RESTful APIs and working with microservices.
• CI/CD & DevOps Tools: Familiarity with Docker, Kubernetes, Jenkins, and other DevOps tools.
• Cloud Services: Strong experience with major cloud platforms (AWS, Google Cloud, Azure), including hands-on experience with AWS Glue, Lambdas, Step Functions, and CloudFormation or Terraform for resource provisioning.
• Software Design Principles: Deep understanding of SOLID principles and common design patterns (e.g., Factory, Strategy, Singleton). Familiarity with Gang of Four (GoF) design patterns is expected, as is the ability to apply these in solution architecture.
   

Soft Skills:

• Strong problem-solving skills and a creative approach to tackling complex challenges. Candidates should be able to clearly articulate their approach to technical problem-solving and system design.
• Effective communication and collaboration skills in cross-functional settings. The ability to concisely explain your role, responsibilities, and contributions to project outcomes is key.
• A mindset for experimentation and innovation.
• Proven experience in stakeholder management, working effectively with product owners, developers, and analysts.
• A strong learning attitude—seeking candidates with high potential who are eager to quickly learn and adapt to new technologies. A foundational understanding of core computer science principles is strongly preferred.
   

Key Responsibilities:

• AI Solution Development: Architect, design, and develop AI-driven applications with an emphasis on reliability, scalability, and performance.
• Model Deployment & Optimization: Partner with data scientists to operationalize and optimize machine learning models for production environments.
• Rapid Prototyping: Lead the full lifecycle of prototypes, iterating quickly to refine solutions based on feasibility and feedback.
• Cloud-Centric Development: Ensure practical hands-on expertise with cloud services and tools such as AWS Glue, Lambdas, Step Functions, and Data Transformations.
• System Architecture Design: Develop scalable architectures that securely and