Job Responsibilities • Enhance multiple products across the product line by identifying, prioritizing, and delivering AI-driven features that improve user experience, efficiency, and operational resilience • Design, build, and deploy production-grade AI applications including LLM-powered workflows, Retrieval-Augmented Generation (RAG) solutions, and agentic systems • Build agentic workflows with enterprise-ready controls (identity, authorization, guardrails, tracing, evaluation, and auditability) • Develop automation solutions that reduce manual effort and eliminate recurring operational issues, including self-healing patterns and intelligent triage • Architect AI systems with production concerns in mind: latency, scalability, cost management, model selection, observability, reliability, and failure modes (including hallucinations and tool errors) • Establish and apply AI engineering standards across the product line (prompting patterns, RAG design standards, evaluation practices, logging/telemetry, and release readiness criteria) • Drive AI architecture reviews and contribute to reference architectures for model hosting, orchestration, data access, and secure tool execution • Identify and mitigate risks across the AI lifecycle including data privacy, security, bias, model misuse, prompt injection, and third-party/vendor risk, and ensure appropriate documentation and controls • Partner with product, design, risk, legal/compliance, and engineering stakeholders to align on outcomes, constraints, and measurable success metrics • Lead technical evaluations with vendors and platform providers; assess capabilities, integration fit, security posture, and total cost of ownership • Research emerging AI capabilities (models, frameworks, tools) and translate findings into practical guidance, prototypes, and adoption recommendations • Educate and enable teams through documentation, workshops, design reviews, and coaching to raise AI fluency and delivery consistency across the organization • Execute creative software solutions, technical troubleshooting, and hands-on development; write secure, high-quality production code and review/debug code written by others • Influence stakeholders and drive alignment across teams without direct authority, owning outcomes end-to-end Required Qualifications, Capabilities, and Skills • Formal training or certification in software engineering concepts and 5+ years of applied engineering experience • Hands-on experience delivering AI-powered features in production or near-production environments (LLMs, RAG, agents, workflow automation, or decision support systems) • Strong proficiency in Python (and ability to build maintainable services, libraries, and pipelines) • Experience building APIs and services using Django, FastAPI, or similar frameworks • Experience building modern UIs using React or similar frameworks (or strong ability to partner closely with frontend teams) • Strong system design skills with an emphasis on secure distributed systems, reliability, and operational excellence • Practical understanding of Responsible AI / model risk concepts (evaluation, guardrails, privacy, bias, explainability where applicable, and safe deployment practices) • Experience implementing observability (logging, metrics, tracing) and AI-specific monitoring (quality, drift signals, latency, cost, error rates) • Experience with cloud platforms (AWS preferred) and containerized deployments • Strong problem-solving skills with a structured, evidence-based approach; comfortable operating in ambiguity and delivering iteratively Preferred Qualifications, Capabilities, and Skills • Experience with AI orchestration and agent frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel, CrewAI, or similar) • Experience with vector search and retrieval patterns including pgvector, Pinecone, Weaviate, OpenSearch/Elastic, or similar • Experience designing and operating RAG systems (chunking strategies, retrieval evaluation, citation/attribution patterns, caching, fallback behaviors) • Familiarity with AI governance and standards (model documentation, risk assessments, control frameworks, secure-by-design patterns) and applying them to real delivery timelines • Strong Infrastructure-as-Code experience (e.g., Terraform) and CI/CD practice, data engineering ecosystems (e.g., Databricks, streaming, data quality controls, lineage concepts)