Senior Software Engineer - AI/ML

Role Overview

The Senior Software Engineer - AI/ML will design, build, and deploy machine learning and generative AI solutions that support key business priorities, partnering closely with stakeholders to define requirements, shape user stories, and deliver production-ready models and applications.

Key Responsibilities

• Collaborate with IT partners, business teams, and the broader IT matrix organization to support solution design and delivery of new digital capabilities, and contribute to the enablement and simplification of existing ones
• Work with IT partners and business teams to understand requirements and use cases; help align requirement outcomes to support successful delivery of new initiatives and enhancements
• Design and develop Generative AI solutions leveraging LLMs for enterprise use cases including text generation, summarization, Retrieval-Augmented Generation (RAG), and intelligent assistants
• Contribute to Agentic AI systems using multi-agent frameworks to help automate workflows and decision-making pipelines
• Break down AI/ML initiatives into user stories, epics, and sprint-ready tasks using Agile/Scrum methodologies
• Gather requirements, propose solution options, and communicate technical concepts clearly to non-technical stakeholders
• Write clean, scalable, production-ready Python code following software engineering best practices
• Support the design of machine learning systems; research and apply appropriate ML algorithms and tools
• Assist with data processing, access, storage, governance, security, and operations
• Help select appropriate datasets and data representation methods for distributed processing and storage
• Contribute to system architecture and design documentation under the guidance of senior engineers/leads
• Run machine learning tests and experiments; train and retrain models as needed
• Perform statistical analysis and fine-tuning based on test results
• Extend existing ML libraries and frameworks while keeping up with developments in the field
• Contribute to Proof of Concepts (POCs), factoring in user management, security, data privacy, and performance considerations
• Help identify high-value capabilities and opportunities for continuous improvement when evaluating platform choices

Desired Experience

Traditional Machine Learning

• Experience building and deploying production ML models using Scikit-learn, NumPy, and Pandas
• Hands-on experience with TensorFlow/Keras for supervised and unsupervised learning tasks
• Experience with feature engineering, model evaluation, and managing the ML lifecycle (e.g., MLflow)

Generative AI

• Hands-on experience building generative AI applications, including prompt engineering and LLM integration
• Experience with agent orchestration frameworks such as LangChain, LangGraph or Claude Agent SDK
• Familiarity with LLM platforms such as Azure OpenAI, AWS Bedrock, or Google Vertex AI
• Working knowledge of vector databases (e.g., pgVector, AWS OpenSearch) for retrieval-augmented generation
• Experience evaluating and monitoring LLM/GenAI application quality using frameworks such as LangSmith

Stakeholder Engagement & Solutioning

• Proven ability to work directly with business stakeholders to gather and define requirements
• Experience translating requirements into clear, actionable user stories
• Ability to evaluate and present multiple solution options with tradeoffs to stakeholders and technical leadership
• Strong communication skills to bridge technical and non-technical audiences

Tools & Collaboration

• Working knowledge of Python and version control (Git)
• Experience working in Agile development environments using tools such as JIRA and Confluence

Agentic AI Development Tools

• Basic understanding of Claude capabilities such as skills, hooks, plugins, subagents, and MCP connectors to build AI solutions
• Ability to use Claude or similar agentic AI coding tools as part of the software development workflow

Good to Have

• Hands-on experience with Databricks, including Databricks MLflow and Databricks AI Search, for ML lifecycle and data workloads
• Experience with cloud ML platforms such as AWS SageMaker
• Exposure to CI/CD tooling (e.g., GitHub Actions, AWS CodePipeline / CodeBuild / CodeDeploy)

Required Qualifications

• Bachelors degree in Computer Science, Data Science, Engineering, or a related field, or equivalent practical experience
• 4+ years of experience in machine learning and/or AI engineering roles
• Demonstrated experience with both traditional ML and generative AI techniques
• Strong analytical and problem-solving skills
• Excellent stakeholder communication and cross-functional collaboration skills
• Experience working in Agile/Scrum environments