• Own the sustaining engineering function for released products throughout their lifecycle • Lead and develop a growing team of sustaining engineers supporting Hyper's expanding product portfolio • Manage engineering change requests (ECRs), engineering change orders (ECOs), deviations, and product modifications • Drive continuous improvement initiatives focused on product reliability, manufacturability, serviceability, quality, and cost optimization • Establish and maintain sustaining engineering processes, metrics, and best practices • Collaborate with New Product Introduction (NPI) teams to ensure smooth product transitions into production • Partner with Manufacturing, Quality, and Supply Chain teams to resolve production issues and prevent recurrence • Support root cause investigations related to manufacturing nonconformances, field failures, and customer concerns • Identify and implement corrective and preventive actions that improve product performance and manufacturing efficiency • Maintain product documentation including bills of material (BOMs), drawings, schematics, assembly instructions, and test procedures • Support component substitutions, obsolescence management, and supplier-driven design changes • Lead root cause analysis and corrective action efforts for customer-reported issues and field failures • Drive resolution of product quality concerns while maintaining customer confidence and satisfaction • Ensure sustained compliance with applicable UL standards, customer specifications, and internal quality requirements • Support audits, product certifications, and regulatory compliance activities as needed • Champion a culture of continuous improvement across engineering and manufacturing operations

• Owns the technical aspect of a domain specific area. • Supervises, coordinates, and reviews work. • Estimates personnel needs and schedules. • Assigns and reviews work to meet completion date. • Evaluate performance, resolve complaints, and make recommendations. • Defines, plans and measures success metrics. • Removes technical and personnel hurdles for team to succeed. • Works with higher-level staff to set expectations and coordinate on work to be delivered.

• Lead privacy engineering initiatives at DuckDuckGo • Own high-complexity privacy projects from definition to delivery • Evolve privacy triage and review processes • Build automation to reduce engineering toil • Grow the next generation of privacy engineers • Contribute to privacy strategy across the company

Role Description Software Mind is building a private, tenant-isolated AI assistant for the real estate title and settlement industry. The platform is a retrieval-first (RAG) system that ingests historical email, documents, and structured metadata into a per-tenant vector index, and serves grounded, cited, expert-weighted answers through a chat-style Q&A interface with single sign-on and full audit logging. The platform is AWS-native with a Python/FastAPI backend, Vue.js frontend, OpenSearch/Pinecone vector store, and OpenAI/Anthropic/Bedrock as LLM provider. You will join a senior, cross-functional LATAM-based team where hands-on AI delivery experience not just familiarity is the baseline expectation. You are the technical delivery lead, the bridge between architectural intent and day-to-day engineering execution. You own code quality, technical decisions within the team, and the delivery of the core AI Extraction Gateway (Simple and Complex RAG). You are hands-on: coding, reviewing, and unblocking across the Python backend and retrieval layers. Responsibilities - Lead hands-on development of the AI Extraction Gateway, progressing from Simple RAG to Complex RAG - Implement and tune the expert-weighted (SME) retrieval layer and structured result validation - Own confidence score calibration; collaborate with the BA on accuracy rubrics and test evidence - Drive technical delivery cadence: sprint planning, code reviews, technical risk identification, and team unblocking - Ensure architectural patterns are implemented consistently across the codebase - Collaborate with the Data Engineer on ingestion pipeline integration points and vector store schema - Implement and evolve the query orchestration layer (Python/FastAPI, AWS Lambda/ECS) - Support the QA Automation Engineer in designing the validation harness for RAG outputs - Maintain development observability: structured logging, CloudWatch dashboards, X-Ray tracing Qualifications - +90% English written and oral (at least B2 level) with excellent communication skills - 6+ years in software development; minimum 2 years in a tech lead or senior engineering lead capacity - Strong Python development skills; FastAPI or equivalent async Python framework required - Hands-on AWS experience: ECS and/or Lambda, API Gateway, DynamoDB, S3, CloudWatch, X-Ray - Experience with vector databases OpenSearch, Pinecone, Weaviate, or equivalent - Solid understanding of API design, service decomposition, and clean backend architecture - AI Experience (Required Not Optional) - Delivered at least one production RAG, semantic search, or LLM-integrated application end-to-end not a prototype or internal tool - Practical experience integrating with LLM provider APIs (OpenAI, Anthropic, or Amazon Bedrock) in a production or enterprise configuration - Working knowledge of chunking strategies, embedding models, retrieval ranking, and prompt engineering in a production context - Experience with confidence scoring, retrieval evaluation, or hallucination mitigation approaches in a deployed system Nice-to-Have - Experience with LangChain, LlamaIndex, or similar LLM orchestration frameworks - Familiarity with OCR pipelines and document extraction tooling (AWS Textract, Tesseract, or equivalent) - Exposure to multi-tenant data isolation patterns and tenant-scoped encryption key management