Senior Software Engineer, AI

Key Responsibilities Applied AI Architecture and Solution Design Lead the technical design of AI‑enabled applications that support enterprise business workflows, including copilots, assistants, and intelligent automations. Define and apply standard patterns for Retrieval Augmented Generation, including data ingestion, chunking strategies, embeddings, vector indexing, retrieval logic, and grounding techniques. Design orchestration approaches for AI applications, including tool calling, agent workflows, and MCP‑style integrations to enterprise systems and services. Partner with Platform teams to ensure AI solutions align with enterprise architecture, security, data governance, and cloud standards. Act as a technical advisor to Product Owners, helping translate business intent into feasible, scalable solution designs. Software Engineering and Integration Build and maintain backend services and APIs, primarily using Python, that support AI applications and integrations. Guide implementation decisions to ensure solutions are secure, scalable, performant, and maintainable in production environments. Review designs and code for AI solutions developed within the team, providing technical direction and setting engineering quality standards. Balance rapid experimentation with disciplined engineering practices to move solutions from prototype to production. Data and Platform Integration Design integration patterns between AI applications and structured and unstructured enterprise data sources, including SQL‑based systems, lakehouse platforms, and document repositories. Collaborate with data engineers and platform teams to ensure data products are fit for AI workloads and aligned with governance requirements. Advise on tooling and platform choices, including Azure AI services and Databricks Apps, based on solution needs and enterprise constraints. Governance, Ethics & Risk Management Design and implement responsible AI principles, including fairness, transparency, explainability, and privacy‑by‑design to be adopted by both platform and enterprise. Partner with IS and Risk stakeholders to create policies to ensure AI solutions comply with internal policies, regulatory requirements, and ethical standards. Lead AI governance forums, design reviews, and risk assessments as required. Collaboration & Continuous Improvement Serve as a senior technical voice within the applied AI practice, shaping standards, design patterns, and reusable components. Mentor and guide other engineers, helping raise overall engineering and AI application maturity. Facilitate technical discussions across teams, helping stakeholders navigate tradeoffs related to architecture, risk, cost, and delivery timelines. Stay current with applied AI and software engineering trends, translating emerging patterns into practical guidance for the organization. Required Qualifications Education: Bachelor’s degree in Computer Science, Engineering, Data Science, Mathematics, or a related field, or equivalent practical experience. Advanced degree is considered an asset. Experience: Senior‑level experience in software engineering roles, with a demonstrated track record of designing and delivering production‑grade systems. Experience leading or influencing technical design decisions across complex solutions, including partnering with platform, architecture, or product teams. Hands‑on experience building backend services and APIs that support business‑critical applications. Experience translating ambiguous business needs into scalable technical solutions, and guiding delivery from concept through production. Experience operating in enterprise environments with cloud platforms, security constraints, and data governance considerations. Technical Skills: Strong proficiency in Python, with experience building and maintaining backend services and APIs. Experience working in cloud environments, preferably Azure, though experience with other major cloud providers is transferable. Experience integrating applications with structured and unstructured data sources, including SQL‑based systems and modern data platforms. Familiarity with applied AI patterns such as Retrieval Augmented Generation, embeddings, vector search, or agent‑style orchestration. Bonus experience with Databricks, Spark, or data engineering concepts such as pipelines and lakehouse architectures. Knowledge & Competencies: Strong software engineering fundamentals, including system design, architecture patterns, and production readiness. Ability to act as a technical advisor, guiding Product Owners and stakeholders through design decisions, tradeoffs, and sequencing. Comfort partnering with platform, data, and security teams to ensure solutions align with enterprise standards. Strong problem‑solving skills and ability to operate effectively in ambiguous, fast‑evolving problem spaces. Clear communicator who can explain complex technical concepts to both technical and non‑technical audiences. Demonstrated ability to mentor other engineers and positively influence technical practices across a team. Preferred Qualifications Experience with large‑scale data platforms, data lakes, or streaming data architectures. Experience supporting AI solutions in regulated or risk‑sensitive environments. Exposure to optimization, forecasting, or decision‑support models. Experience working in agile or product‑oriented delivery models. Equal Opportunity Employer We are proud to be an Equal Opportunity Employer and are committed to providing employment opportunities to all qualified individuals without regard to age, race, color, national or ethnic origin, religion, sex, sexual orientation, gender identity or expression, veteran status, genetic information, disability, or any other characteristic protected by federal, state, or local law. Applicants with disabilities can request accessible formats, communication supports, or other accessibility assistance by contacting View email address on click.appcast.io. #J-18808-Ljbffr