Technical Lead

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections if posting has real specific content. We must not repeat job title. Must not include Job ID etc. Must preserve all substantive info. Remove boilerplate like cookie/navigation/legal/EEO etc. Also remove duplicated lines, application instructions. Must not include sections with filler like "commensurate with experience". Pay and Benefits: posting includes Pay Range and also Pay And Benefits section describing benefits. So we can include Pay section with the range, and Benefits section with description of benefits (competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement). Also maybe include About the role? The description includes a paragraph about role. Could be "About the role". Responsibilities: there is a list of key responsibilities. Requirements: there is "Required Qualifications". Qualifications: there is "Preferred Qualifications". Skills: maybe combine required and preferred? But we can have Skills section summarizing technical skills? However we must not invent. The posting includes many specific skills/expertise. Could put them under Skills. But we also have Requirements and Qualifications sections. We need to decide what to put where. We have: - About the role: first paragraph describing role. - Responsibilities: bullet list under "Key Responsibilities". We'll output

• each. - Requirements: maybe "Required Qualifications" list. - Qualifications: maybe "Preferred Qualifications" list. - Skills: Could be derived from required/preferred but we might avoid duplication. However instruction: Prefer a few substantive sections over many thin ones. Could combine Required and Preferred into Qualifications? But they ask to use headings for sections like Requirements, Qualifications, Skills. We could put Required Qualifications under Requirements, Preferred Qualifications under Qualifications, and maybe Skills under Skills (like specific technologies). But that may duplicate content. However we must preserve ALL substantive information; we can distribute but not repeat same content in multiple sections? It didn't forbid repeating but better to avoid duplication. Could put all under Requirements and Qualifications and omit Skills. But they said include a section ONLY when posting has real, specific content for it. So we can include Skills if we have specific content. The posting includes many specific technologies: TensorFlow, PyTorch, etc. That could be Skills. But those are also in Required Qualifications (expertise). Might be okay to put them under Skills and keep Required Qualifications more about experience/education. Let's parse: Required Qualifications bullet points: - Master’s degree in ... - Minimum 15 years ... - Minimum 10 years designing, implementing, and leading enterprise AI and ML solutions ... - Demonstrated experience supporting NIH, NCI, or other Federal biomedical research organizations. - Extensive expertise in deep learning, machine learning, generative AI, NLP, LLMs, computer vision, predictive analytics, biomedical informatics, and cloud-native AI architectures. - Demonstrated experience advising executive Government customers on AI strategy, enterprise architecture, and digital modernization initiatives. - Proven experience leading multidisciplinary teams ... - Experience with HPC, cloud-native AI platforms, Kubernetes, DevSecOps, and MLOps. - Familiarity with TensorFlow, PyTorch, Scikit-learn, Hugging Face, MLflow, Kubeflow, LangChain, vector databases, graph databases, and AI orchestration frameworks. - Ability to obtain and maintain a Public Trust / ADPII Clearance. Preferred Qualifications: - Ph.D. in AI, Biomedical Informatics, Computer Science, Computational Biology, or related field preferred. - Experience supporting NCI organizations including CBIIT, CCR, DCTD, DCP, or related biomedical research programs. - Experience with cancer informatics, biomedical imaging, radiomics, genomics, precision oncology, clinical trials, and translational research. - Expertise with FHIR, OMOP, FAIR data principles, biomedical ontologies, semantic interoperability, and research data ecosystems. Desired Characteristics: list of soft skills. Thus we could have: - Requirements: education, years experience, supporting NIH, advising exec, leading teams, clearance. - Qualifications: preferred qualifications (PhD, NCI experience, cancer informatics, FHIR etc.) - Skills: technical expertise list (deep learning, ML, generative AI, NLP, LLMs, computer vision, predictive analytics, biomedical informatics, cloud-native AI architectures, HPC, cloud-native AI platforms, Kubernetes, DevSecOps, MLOps, TensorFlow, PyTorch, Scikit-learn, Hugging Face, MLflow, Kubeflow, LangChain, vector databases, graph databases, AI orchestration frameworks). Also maybe include FHIR etc? But those are in Qualifications. But we must not invent. We'll just extract. Benefits: from "Pay And Benefits" section: "Employment benefits include competitive compensation, Health and Wellness programs, Income Protection, Paid Leave and Retirement." Also maybe more details at website but we only include that. Pay: Pay Range $131,300.00 - $237,350.00. Schedule: not mentioned. So we omit Schedule section. Now produce HTML:

  About the role

  ... paragraph ...

  Responsibilities

  • ... each bullet ...

  Requirements

  • ... each required qualification bullet ...

  Qualifications

  • ... each preferred qualification bullet ...

  Skills

  • ... each skill bullet ...

  Benefits

  • ... each benefit bullet ...

  Pay

  Pay Range $131,300.00 - $237,350.00

  We must not include any extra text. Ensure we preserve all substantive info. Let's extract exact sentences. About the role paragraph: "The Leidos Health & Civil Sector is seeking a Technical Lead to support the National Institutes of Health (NIH), National Cancer Institute (NCI), Center for Biomedical Informatics and Information Technology (CBIIT) Business Information Technology Solutions Development and Integration Services (BITSDIS) 2 program. ** This posting is for future business – not yet awarded ** The Technical Lead will provide strategic technical leadership, scientific consulting, and executive-level advisory support to Government leadership for the development, integration, and operationalization of advanced Artificial Intelligence (AI), Machine Learning (ML), and data science capabilities that accelerate cancer research, clinical decision support, and precision medicine. Serving as the Government's trusted technical advisor, the Technical Lead will collaborate closely with NCI leadership, principal investigators, biomedical researchers, clinicians, informaticians, and multidisciplinary engineering teams to develop innovative AI-enabled solutions supporting clinical research, scientific data management, and enterprise research platforms. The successful candidate will combine deep expertise in artificial intelligence, machine learning, biomedical informatics, cloud computing, and modern software engineering with exceptional customer engagement and communication skills to guide technology strategy, influence architectural decisions, and ensure delivery of scalable, secure, and scientifically rigorous AI capabilities aligned with NIH and NCI strategic priorities." We need to keep that but remove the "** This posting is for future business – not yet awarded **" maybe considered boilerplate? It's not exactly boilerplate but it's a note about future business. It's substantive? It says it's for future business - not yet awarded. That's info about the posting. Should we keep? The instruction: remove cookie/navigation/legal/EEO/equal-opportunity/scam-warning boilerplate, duplicated lines, and application instructions. This note is not those; it's about the posting. I'd keep it but maybe it's considered filler? It's specific. I'd keep it. Thus About the role paragraph includes that sentence. We'll keep it as is. Responsibilities bullets: need to capture each bullet from "Key Responsibilities". Let's list: - Lead the technical strategy, architecture, and implementation of AI-enabled solutions that support cancer research, precision medicine, clinical research, and enterprise biomedical informatics. - Serve as the principal technical advisor to NCI leadership, providing strategic guidance on AI, ML, biomedical informatics, and digital modernization initiatives. - Partner with Government stakeholders, scientific leadership, and multidisciplinary Subject Matter Experts (SMEs) to define technical priorities, evaluate emerging technologies, and deliver innovative, mission-focused solutions. - Provide technical leadership across AI model development, deployment, and lifecycle management, including machine learning, deep learning, natural language processing (NLP), large language models (LLMs), computer vision, predictive analytics, and multi-agent AI systems. - Oversee the design and integration of AI capabilities supporting biomedical imaging, radiomics, genomics, multi-omics analysis, precision oncology, clinical trial optimization, semantic search, knowledge management, and decision support. - Guide the development of scalable AI and cloud-based architectures leveraging high-performance computing (HPC), AWS, Azure, Google Cloud Platform, Kubernetes, and modern MLOps practices. - Ensure AI solutions adhere to Responsible AI principles, including model governance, explainability (XAI), bias mitigation, security, privacy, and compliance with NIH policies, Executive Orders, and the NIST AI Risk Management Framework. - Lead technical reviews, architecture discussions, executive briefings, and technology roadmaps while effectively communicating complex technical concepts to Government executives, researchers, and business stakeholders. - Mentor engineering teams and provide technical oversight to AI engineers, software developers, data scientists, cloud architects, cybersecurity specialists, and biomedical informatics SMEs to ensure successful delivery of high-quality technical solutions. - Evaluate and recommend emerging AI technologies and innovative approaches that accelerate scientific discovery, improve operational efficiency, and advance NCI's strategic research mission. We'll output each as
• . Requirements (Required Qualifications) bullets: - Master’s degree in Computer Science, Artificial Intelligence, Biomedical Informatics, Bioinformatics, Data Science, Computer Engineering, Applied Mathematics, or a related discipline. - Minimum 15 years of progressively responsible technical leadership supporting large-scale Federal health, biomedical research, or scientific computing programs. - Minimum 10 years designing, implementing, and leading enterprise AI and ML solutions within healthcare, biomedical research, life sciences or federal research environments. - Demonstrated experience supporting NIH, NCI, or other Federal biomedical research organizations. - Extensive expertise in deep learning, machine learning, generative AI, NLP, LLMs, computer vision, predictive analytics, biomedical informatics, and cloud-native AI architectures. - Demonstrated experience advising executive Government customers on AI strategy, enterprise architecture, and digital modernization initiatives. - Proven experience leading multidisciplinary teams consisting of AI engineers, data scientists, software engineers, cloud architects, cybersecurity specialists, and biomedical subject matter experts. - Experience with HPC, cloud-native AI platforms, Kubernetes, DevSecOps, and MLOps. - Familiarity with TensorFlow, PyTorch, Scikit-learn, Hugging Face, MLflow, Kubeflow, LangChain, vector databases, graph databases, and AI orchestration frameworks. - Ability to obtain and maintain a Public Trust / ADPII Clearance. Qualifications (Preferred Qualifications) bullets: - Ph.D. in AI, Biomedical Informatics, Computer Science, Computational Biology, or related field preferred. - Experience supporting NCI organizations including CBIIT, CCR, DCTD, DCP, or related biomedical research programs. - Experience with cancer informatics, biomedical imaging, radiomics, genomics, precision oncology, clinical trials, and translational research. - Expertise with FHIR, OMOP, FAIR data principles, biomedical ontologies, semantic interoperability, and research data ecosystems. Skills: we could list the technical expertise from Required Qualifications (extensive expertise etc.) and familiarity with specific frameworks. But to avoid duplication, maybe we list the specific technologies: deep learning, machine learning, generative AI, NLP, LLMs, computer vision, predictive analytics, biomedical informatics, cloud-native AI architectures, HPC, cloud-native AI platforms, Kubernetes, DevSecOps, MLOps, TensorFlow, PyTorch,