Senior Lead Software Engineer- Python AI

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)