Role Description SMX is seeking an accomplished Solution Architect for designing, integrating, and implementing Oracle E-Business Suite (EBS) solutions by analyzing business needs, collaborating with stakeholders, and ensuring the functional and technical architecture aligns with strategic goals. This is a remote role supporting a Lackland, TX program requiring some travel. - Solution design: Create and oversee the architecture for Oracle EBS solutions, ensuring they align with business objectives and integrate seamlessly with other enterprise applications and systems. - Technical leadership: Provide expert guidance on the overall design and configuration of EBS, including custom extensions, reports, and integrations. - Business-technical alignment: Bridge the gap between business stakeholders and technical teams, translating business requirements into technical solutions. - Project support: Collaborate with project managers, developers, and business analysts to ensure project deliverables meet timelines and quality standards. - System optimization: Assess existing systems, identify areas for improvement, and ensure the scalability and security of the solution. - Stakeholder engagement: Lead workshops to educate stakeholders on new technologies, best practices, and the capabilities of the Oracle EBS platform. Qualifications - Experience in implementing Oracle EBS R12 General Ledger (GL) module along with chart of account (COA) design. - Extensive experience with Oracle EBS, particularly Oracle E-Business Suite R12 and procurement modules. - Experience advising Oracle RICE component process. - Deep knowledge of ERP systems, solution design, and integration patterns. - Proficiency in relevant technologies, which can include SQL, PL/SQL, Oracle SOA, and potentially programming languages like Java or Python. - Ability to understand and translate complex business processes into technical solutions. - Strong communication, collaboration, and problem-solving skills are essential for working with diverse teams and stakeholders. - A bachelor's degree in computer science, information technology, or a related field. Additional years of experience in lieu of degree will be considered. - Minimum of 16 years’ experience. Directly related postgraduate study may be substituted for experience at the rate of three credit hours for three months of experience up to 36 credit hours. - US Citizen with an active Public Trust clearance. Requirements - Contracting LifeCycle Management (CLM), Procurement, iSupplier (or similar product), and Purchasing modules experience. - Oracle EBS core financial accounting knowledge with a concentration in Projects Management, experience to include financial accounting experience for transition of legacy financial statements and interrelated data functionality from legacy system to the modules. - Experience with developing web services and integrating applications using tools like Oracle Integration Cloud (OIC). Benefits - Health insurance. - Paid leave. - Retirement. Application Deadline 9-4-2026

• Define and own the architecture strategy for Airvoyant's AI-powered platform. • Design scalable solutions leveraging Generative AI, Agentic AI, RAG, and intelligent automation. • Lead architecture for cloud-native applications, APIs, microservices, and event-driven systems. • Establish architectural standards, governance, security, and best practices. • Partner with Product, Engineering, and Data Science teams to translate business needs into scalable technical solutions. • Drive technology selection, proof-of-concepts, and architecture reviews. • Mentor engineering teams and provide technical leadership across the organization. • Design integrations with aviation, MRO, ERP, and supply chain systems.

• Definir y validar arquitecturas E2E (frontend, middleware, backend) en entornos distribuidos. • Diseñar y evolucionar soluciones de integración y APIs (REST & Async) con Mulesoft. • Liderar equipos y actuar como referente técnico en la toma de decisiones. • Coordinar stakeholders y asegurar buenas prácticas, calidad y estándares. • Supervisar la implementación y detectar riesgos técnicos proponiendo soluciones.

Role Description Highly specialized Principal Scientific Data Architect to bridge the gap between advanced Google Cloud engineering and life sciences discovery. This role will redefine how scientific data is structured, scaled, and consumed across our R&D, Onyx, and CMC (Chemistry, Manufacturing, and Controls) divisions. Operating natively within the Google Cloud Platform (GCP) and Databricks on GCP ecosystem, will lead the transition toward a fully automated, software-defined data framework by implementing: - Schema as Code - Data as Code - Metadata-driven Configuration Data Engineering The ideal candidate combines elite cloud data architecture expertise with deep scientific literacy, enabling the design of data systems that directly power in-silico molecular discovery and autonomous Agentic AI frameworks. Key Responsibilities - GCP-Native Data Architecture & Paradigm Shifts - Schema as Code: Design and implement version-controlled, programmatically managed data schemas natively integrated with Google BigQuery. Ensure schemas evolve seamlessly using GCP DevOps tools (Cloud Build, Artifact Registry) and Terraform. - Data as Code: Treat data assets with software engineering rigor. Implement data versioning, programmability, and automated quality testing using BigQuery features (like Table Snapshots and Time Travel), dbt, and Delta Lake on GCP. - Configuration Data Engineering: Architect highly optimized, metadata-driven, configuration-led data pipelines using Google Cloud Composer (Airflow) or Dataflow to abstract infrastructure complexity. - Scientific Domain Integration - Translate complex biological and chemical concepts (e.g., molecular modalities, chemical structures, solubility traits) into highly scalable logical and physical data models within BigQuery and Databricks. - Collaborate closely with computational chemists, biologists, and AI engineers to ensure the data architecture natively supports predictive in-silico modeling. - Design robust data layouts that allow autonomous AI agents to easily "dip into" molecular data, extract properties, and explain molecular behavior. - Platform & Ecosystem Strategy - Optimize the interoperability between Databricks on GCP (Lakehouse architecture) and enterprise-wide Google BigQuery storage and analytics. - Inform the integration of semantic web technologies and knowledge graphs (e.g., StarDog) into the overarching Google Cloud data fabric. - Ensure data availability and high-performance querying for downstream multi-agent AI ecosystems (Agentic Hubs built on Google Cloud's AI suite or custom frameworks). Qualifications - Scientific Domain Knowledge: Strong background or proven experience working inside life sciences, pharmaceuticals, biotech, or scientific research organizations. - Ability to converse fluently with scientists regarding therapeutic modalities, molecular properties, and R&D pipelines without needing to be a wet-lab scientist. - GCP & Technical Architecture Expertise: - Mastery of Google BigQuery (including BigLake, analytics hubs, and nested JSON schemas) and Databricks on GCP. - Proven track record of implementing Schema as Code and Data as Code paradigms using tools like Terraform, dbt, and Git-based CI/CD workflows. - Deep experience with configuration-driven pipeline orchestrators, specifically Google Cloud Composer / Apache Airflow. - Strong understanding of relational, dimensional, and graph-based data modeling. Familiarity with knowledge graphs (e.g., StarDog) or biomedical ontologies is a major plus. - Soft Skills & Leadership: - Ability to conceptualize and suggest complex in-silico data solutions at a high strategic level without getting bogged down by immediate technology limitations. - Exceptional ability to articulate the business and scientific value of pure data architecture to non-technical executive stakeholders. Preferred Qualifications - Professional Google Cloud Data Engineer or Google Cloud Professional Cloud Architect certification. - Degree in Computer Science, Data Engineering, Bioinformatics, Computational Chemistry, or a related quantitative field. - Experience setting up GCP data foundations specifically engineered to feed Large Language Models (e.g., Vertex AI / Gemini) and autonomous AI agents.