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Drive significant business impact through your capabilities and contributions, and apply deep technical expertise and problem-solving methodologies to tackle a diverse array of challenges that span multiple technologies and applications.\nJob responsibilities\nRegularly provides technical guidance and direction to support the business and its technical teams, contractors, and vendors\nDevelops secure and high-quality production code, and reviews and debugs code written by others\nDrives decisions that influence the product design, application functionality, and technical operations and processes\nServes as a function-wide subject matter expert in one or more areas of focus\nActively contributes to the engineering community as an advocate of firmwide frameworks, tools, and practices of the Software Development Life Cycle\nInfluences peers and project decision-makers to consider the use and application of leading-edge technologies\nAdds to the team culture of opportunity, inclusion, and respect\nAbility to tackle design and functionality problems independently with little to no oversight\nDrives team adoption of enterprise-authorized AI-assisted engineering practices within the work environment to improve code quality, delivery speed, and operational outcomes (e.g., AI-assisted code review/refactoring, test strategy acceleration, incident/root-cause analysis support), while establishing consistent validation standards (secure coding, peer review, automated testing) and promoting reuse of effective patterns across the team.\nApplies knowledge of tools within the Software Development Life Cycle toolchain, including enterprise-authorized AI-assisted development and automation capabilities, to improve the value realized by automation.\nRequired qualifications, capabilities, and skills\nFormal training or certification on software engineering concepts and 10+ years applied experience\nAdvanced in programming languages Java, Python or Node.js\nStrong proficiency with RAG architectures - chunking strategies, embedding models, vector stores (Pinecone, OpenSearch, pgvector, FAISS)\nExperience with AI orchestration frameworks: LangChain, LlamaIndex, Semantic Kernel, or CrewAI\nHands-on experience with AWS Bedrock, Anthropic Claude models, and model invocation APIs\nProven prompt engineering skills - system prompts, few-shot, chain-of-thought, tool use, structured outputs\nExperience building conversational AI: chatbots (text) and voicebots (speech-to-text, text-to-speech integration)\nProficiency with AWS services (Lambda, Step Functions, API Gateway, S3, DynamoDB, SQS)\nExperience with CI/CD pipelines, containerization (Docker, ECS/EKS), and infrastructure-as-code\nStrong understanding of API design (REST, GraphQL), microservices architecture, and event-driven systems\nFamiliarity with evaluation frameworks for LLM outputs. 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