AI/ML Engineer - Multiple levels (Cleared)

We need to output HTML fragment with sections: Responsibilities, Required Qualifications, Desired Qualifications, maybe Overview, Why Work at Noblis, Total Rewards? The instruction: include a section ONLY when posting has real, specific content for it. We must not include sections like Pay/Schedule/Benefits unless posting actually states them. The posting includes compensation ranges for each level and overall posted salary range. So we have compensation info. Should we include a Pay section? The rule: "never create Pay/Schedule/Benefits sections unless the posting actually states them." The posting does state compensation ranges, so we can include a Pay section. However we need to decide if we should include a Pay section with the ranges. The posting includes multiple compensation ranges per level and overall posted salary range. We should preserve all substantive information. So we can include a Pay section with those details. Schedule: not mentioned. Benefits: there is a Total Rewards section describing benefits. That is substantive. So we can include a Benefits section (maybe using the heading Benefits). The posting includes "Total Rewards" with description of benefits. So we can include Benefits. We should not include sections like "About the role" unless present. There's an "Overview" section describing Noblis. That could be considered About the role? The instruction says use

for section headings (e.g. About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule). So we can map sections: Responsibilities, Required Qualifications, Desired Qualifications, Overview (maybe as About the role?), Why Work at Noblis (maybe as About the role? but we already have Overview). Could also include "Why Work at Noblis" as a separate section maybe "About the role"? But the instruction says prefer a few substantive sections over many thin ones. We could combine Overview and Why Work at Noblis into a single "About the role" or "Overview". However we need to preserve all substantive information; we can keep them as separate sections if they are substantive. But the instruction says prefer a few substantive sections over many thin ones. We have many sections: Responsibilities, Required Qualifications, Desired Qualifications, Overview, Why Work at Noblis, Total Rewards, Pay. That's 7 sections. Could be okay if each is substantive. But we might combine Overview and Why Work at Noblis into one "About the role" section. However the instruction says use headings like About the role, Responsibilities, etc. So we could use About the role for the overview and why work at Noblis combined. Then we have Responsibilities, Required Qualifications, Desired Qualifications, Benefits, Pay. That's 5 sections. We need to ensure we don't repeat job title as heading. No Job ID. We need to use

• for any list of duties/requirements/benefits. For prose, use

  . We need to preserve all substantive information, not invent or summarize away detail. So we need to include details from each section. Let's extract content. Responsibilities: The posting has a "Responsibilities" heading with subheadings: Key Responsibilities, then bullet points under Model Development & Deployment, Infrastructure & Operations, Technical Leadership. We need to convert to

  • . Probably each bullet point is a sentence. We'll need to capture them. Let's list responsibilities: - Design, develop, and containerize machine learning (ML) models using modern frameworks and tools, including PyTorch, Ray, Docker, and FastAPI - Deploy, manage, and scale production ML workloads on Kubernetes - Integrate AI/ML capabilities into full-stack applications using Python-based backend services and JavaScript frontend technologies - Ensure model reliability, performance, and maintainability throughout the deployment lifecycle - Architect and implement cloud-native ML infrastructure on AWS - Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring - Deploy and support AI/ML systems within secure, classified, and high-side environments - Evaluate and integrate state-of-the-art AI/ML models, frameworks, and emerging technologies to enhance mission capabilities and accelerate innovation - Architect scalable, resilient, and secure infrastructure to support evolving AI/ML workloads, production deployments, and mission-critical requirements - Establish and champion best practices for production-grade machine learning (ML) systems, including MLOps, security, observability, and governance - Provide technical guidance and thought leadership across AI/ML initiatives and engineering teams That's the list. Required Qualifications: The posting includes bullet-like paragraphs: - Active Top Secret/SCI (TS/SCI) with Polygraph - S. Citizenship is required - Travel up to 10% within US. - Lift up to 30 lbs, walk, bend, drive. - Junior level Bachelor's, Master's, or PhD degree with 0-3 years of related experience including exposure or coursework in machine learning concepts and frameworks; OR Associate's degree with 3 years of related experience; OR High School diploma/GED with 6 years of related experience - Knowledge of machine learning frameworks and deploying ML models to production - Compensation: $90,700 - $171,525 (but compensation is separate; we might not include in Required Qualifications; but it's listed there. However we will have a Pay section separate; we should not duplicate. The instruction: preserve ALL substantive information; do NOT invent or summarize away detail. If we move compensation to Pay section, we need to still preserve it; we can omit from Required Qualifications to avoid duplication. But we need to check if the compensation is part of Required Qualifications subsection for Junior level. It appears after Junior level description, there is a line "Compensation: $90,700 - $171,525". Then Mid-level etc. So the compensation is tied to each level. Better to keep compensation in Pay section, not duplicate. We'll extract compensation info for each level and overall. Thus Required Qualifications should include: Active TS/SCI with Polygraph, US Citizenship, Travel up to 10% within US, Lift up to 30 lbs, walk, bend, drive, plus the education/experience requirements for each level? Actually the Required Qualifications section includes Junior, Mid-level, Senior level descriptions each with education/experience and compensation. Might be better to treat Required Qualifications as the general requirements (clearance, citizenship, travel, physical) and then have separate sections for each level? But the instruction says use headings like Requirements, Qualifications, Skills. We have Required Qualifications and Desired Qualifications. So we can put the education/experience requirements under Required Qualifications (maybe as a list). However the posting splits by level; we could keep them as sub-items. We need to preserve all substantive information; we can list each level's education/experience as separate
  • items. Thus Required Qualifications list: - Active Top Secret/SCI (TS/SCI) with Polygraph - U.S. Citizenship required - Travel up to 10% within the United States - Ability to lift up to 30 lbs, walk, bend, drive - Junior level: Bachelor's, Master's, or PhD degree with 0-3 years of related experience including exposure or coursework in machine learning concepts and frameworks; OR Associate's degree with 3 years of related experience; OR High School diploma/GED with 6 years of related experience - Mid-level: Bachelor's degree with 5 years of related experience; OR Master's degree with 3 years of related experience; OR Associate's degree with 8 years of related experience; OR High School diploma/GED with 11 years of related experience - Senior level: Bachelor's degree with 8 years of related experience; OR Master's degree with 6 years of related experience; OR Associate's degree with 11 years of related experience; OR High School diploma/GED with 14 years of related experience - Knowledge of machine learning frameworks and deploying ML models to production We could also include that the knowledge of ML frameworks is required for all levels (stated under each level). We'll include as a separate item. Desired Qualifications: list of items: - Full-stack software development experience using Python and JavaScript - Expert-level proficiency in Python with extensive experience across leading machine learning (ML) frameworks, including TensorFlow, PyTorch, and scikit-learn - Proven ability to design and implement end-to-end machine learning (ML) pipelines, spanning data ingestion, feature engineering, model training, evaluation, deployment, and monitoring - Extensive experience with large language models (LLMs), including fine-tuning, prompt engineering, retrieval-augmented generation (RAG), agentic workflows, and responsible AI practices - Expertise in advanced machine learning (ML) techniques, including deep learning, reinforcement learning, generative models, ensemble methods, and modern model optimization approaches - Proven track record of designing and implementing production-grade MLOps infrastructure, including automated model retraining, monitoring, drift detection, and CI/CD pipelines using tools such as MLflow, Kubeflow, and SageMaker Pipelines - Hands-on experience architecting and deploying scalable machine learning (ML) solutions on cloud platforms (e.g., AWS SageMaker, Azure Machine Learning, Google Vertex AI) with a focus on scalability, reliability, and cost optimization - Demonstrated experience leading technical architecture decisions and mentoring engineers on machine learning (ML) best practices, software engineering standards, experimentation, code quality, and research methodology - Strong background in distributed computing and big data technologies such as Apache Spark, Ray, and Dask for efficient model training and inference - Proficiency with containerization and orchestration technologies, including Docker and Kubernetes, to support scalable model serving, A/B testing, and canary releases/deployments - Demonstrated ability to translate complex business problems into well-scoped ML solutions, communicating trade-offs, risks, and ROI to executive stakeholders - Experience contributing to or publishing applied ML research, patents, conference presentations, or open-source projects Overview: The posting has "Overview" paragraph about Noblis and subsidiaries, mission, etc. Then "Why Work at Noblis" paragraph about culture, etc. Then "Total Rewards" paragraph about benefits. We'll combine Overview and Why Work at Noblis into About the role section? Or we could keep Overview as About the role and Why Work at Noblis as a separate section maybe "Why Work at Noblis". But the instruction says prefer a few substantive sections over many thin ones. We could merge them into About the role. Thus About the role section will contain the paragraphs from Overview and Why Work at Noblis. Benefits: The Total Rewards section describes benefits: health, life, disability, financial, retirement benefits, paid leave, professional development, tuition assistance, work-life programs, award programs, eligibility, etc. We'll put that in Benefits section as a
    • list? It's prose; we can use

      for prose and maybe

      • for list of offerings. The instruction: Use
        • for any list of duties/requirements/benefits. So we should list benefits as
          • . Let's extract benefits list: - health, life, disability, financial, and retirement benefits - paid leave - professional development - tuition assistance - work-life programs - award programs acknowledge employees for exceptional performance and superior demonstration of service standards - Full-time and part-time employees working at least 20 hours a week on a regular basis are eligible to participate in our benefit programs. - Other offerings may be provided for employees not within this category. - Encouraged to learn more about total benefits by visiting the Benefits page on our Careers site. We'll put each as
          • . Pay: We need to include compensation ranges. The posting includes compensation per level and overall posted salary range. We'll list them. We could list: - Junior level: $90,700 - $171,525 per year - Mid-level: $132,900 - $207,750 per year - Senior level: $160,800 - $251,32