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assess capabilities, integration fit, security posture, and total cost of ownership\nResearch emerging AI capabilities (models, frameworks, tools) and translate findings into practical guidance, prototypes, and adoption recommendations\nEducate and enable teams through documentation, workshops, design reviews, and coaching to raise AI fluency and delivery consistency across the organization\nExecute creative software solutions, technical troubleshooting, and hands-on development; write secure, high-quality production code and review/debug code written by others\nInfluence stakeholders and drive alignment across teams without direct authority, owning outcomes end-to-end\nRequired Qualifications, Capabilities, and Skills\nFormal training or certification in software engineering concepts and 5+ years of applied engineering experience\n Hands-on experience delivering AI-powered features in production or near-production environments (LLMs, RAG, agents, workflow automation, or decision support systems)\nStrong proficiency in Python (and ability to build maintainable services, libraries, and pipelines)\nExperience building APIs and services using Django, FastAPI, or similar frameworks\nExperience building modern UIs using React or similar frameworks (or strong ability to partner closely with frontend teams)\nStrong system design skills with an emphasis on secure distributed systems, reliability, and operational excellence\nPractical understanding of Responsible AI / model risk concepts (evaluation, guardrails, privacy, bias, explainability where applicable, and safe deployment practices)\nExperience implementing observability (logging, metrics, tracing) and AI-specific monitoring (quality, drift signals, latency, cost, error rates)\nExperience with cloud platforms (AWS preferred) and containerized deployments\nStrong problem-solving skills with a structured, evidence-based approach; 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