Senior Information Solution Architect

1 Day ago • 7 Years +
Data Analysis

Job Description

Kavaliro is seeking a Senior Information Solution Architect to design, build, and support production-level AI/ML systems. The role involves re-architecting data scientist models for scale, efficiency, security, and reliability in operational environments. Key responsibilities include creating training and deployment pipelines, APIs, and monitoring systems, ensuring compliance with DoD and CMMC standards. The architect will also evaluate software requirements, propose technical solutions, contribute to long-term architecture strategies, develop design documentation, conduct code reviews, and mentor junior developers.
Good To Have:
  • Experience with geospatial applications and interactive map design.
  • Knowledge of modern front-end tools (Node.js, Angular/AngularJS, TypeScript, Flow).
  • Familiarity with CSS preprocessors (Sass, Less) and build systems (Webpack, Rollup, Grunt, Gulp).
  • Experience with Agile development practices and tools like Jira.
  • Relevant technical or Agile certifications (e.g., Certified Scrum Master, Microsoft certifications, SAFe).
  • Exposure to AWS services and additional technical certifications.
Must Have:
  • Develop, deploy, and maintain machine learning systems in production environments.
  • Convert prototype ML models into scalable, optimized solutions.
  • Design and implement full ML pipelines (training, evaluation, deployment, monitoring, retraining).
  • Build APIs and microservices to expose models to applications and end-users.
  • Apply MLOps practices, including CI/CD, model registries, version control, and reproducibility.
  • Monitor system health, model drift, and data drift, implementing retraining triggers.
  • Optimize ML models for latency, throughput, and cost.
  • Work with data engineers and systems engineers for cloud deployment.
  • Ensure all systems meet performance, security, and compliance requirements.
  • Prepare design documentation, white papers, and system diagrams.
  • Participate in QA reviews, debugging, testing, and performance validation.
  • Collaborate with project managers to align schedules, resources, and technical direction.
  • Mentor less experienced team members and promote adherence to coding standards.
  • Bachelor’s degree in Computer Science, Mathematics, or related technical field.
  • 7+ years of relevant experience in software engineering for DoD or commercial projects.
  • Strong programming skills in Python, C++, and Java.
  • Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or JAX.
  • Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI).
  • Experience with containerization and orchestration (Docker, Kubernetes).
  • Proven ability to deploy and manage workloads in cloud environments (AWS, GCP, Azure).
  • Knowledge of monitoring and observability tools for ML systems.
  • Experience implementing CI/CD pipelines using tools like Jenkins, Maven, and Git.
  • Skilled in model optimization techniques and performance tuning.
  • Ability to work directly with SMEs and customers to clarify requirements and design solutions.
  • Strong problem-solving skills with the ability to independently resolve complex technical challenges.
  • Knowledge of DoD secure coding requirements and compliance standards.
  • Must be a U.S. Citizen; active DoD clearance preferred or ability to obtain and maintain clearance.

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Kavaliro is seeking a Senior Information Solution Architect for our client who will be focused on designing, building, and supporting production-level AI/ML systems. The engineer will take models developed by data scientists, re-architect them for scale, and ensure they can run efficiently, securely, and reliably in operational environments. Core responsibilities include creating training and deployment pipelines, APIs, and monitoring systems, while maintaining compliance with DoD and CMMC standards. The position also involves evaluating software requirements, proposing technical solutions, and contributing to long-term architecture strategies. In addition, the engineer will develop design documentation, conduct code reviews and quality checks, and provide mentorship to junior developers.

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Key Responsibilities

  • Develop, deploy, and maintain machine learning systems in production environments.
  • Convert prototype ML models into scalable, optimized solutions ready for operational use.
  • Design and implement full ML pipelines (training, evaluation, deployment, monitoring, retraining).
  • Build APIs and microservices to expose models to applications and end-users.
  • Apply MLOps practices, including CI/CD, model registries, version control, and reproducibility.
  • Monitor system health, model drift, and data drift, implementing retraining triggers where needed.
  • Optimize ML models for latency, throughput, and cost (e.g., quantization, distillation, distributed training).
  • Work with data engineers on data pipelines and with systems engineers to ensure smooth cloud deployment.
  • Ensure all systems meet performance, security, and compliance requirements.
  • Prepare design documentation, white papers, and system diagrams to support technical and program goals.
  • Participate in QA reviews, debugging, testing, and performance validation.
  • Collaborate with project managers to align schedules, resources, and technical direction.
  • Mentor less experienced team members and promote adherence to coding standards and best practices.

---

Required Skills & Experience

  • Bachelor’s degree in Computer Science, Mathematics, or related technical field.
  • 7+ years of relevant experience in software engineering for DoD or commercial projects.
  • Strong programming skills in Python, C++, and Java.
  • Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or JAX.
  • Familiarity with MLOps tools (MLflow, Kubeflow, SageMaker, Vertex AI).
  • Experience with containerization and orchestration (Docker, Kubernetes).
  • Proven ability to deploy and manage workloads in cloud environments (AWS, GCP, Azure).
  • Knowledge of monitoring and observability tools for ML systems.
  • Experience implementing CI/CD pipelines using tools like Jenkins, Maven, and Git.
  • Skilled in model optimization techniques and performance tuning.
  • Ability to work directly with SMEs and customers to clarify requirements and design solutions.
  • Strong problem-solving skills with the ability to independently resolve complex technical challenges.
  • Knowledge of DoD secure coding requirements and compliance standards.
  • Must be a U.S. Citizen; active DoD clearance preferred or ability to obtain and maintain clearance.

---

Preferred Qualifications

  • Experience with geospatial applications and interactive map design.
  • Knowledge of modern front-end tools (Node.js, Angular/AngularJS, TypeScript, Flow).
  • Familiarity with CSS preprocessors (Sass, Less) and build systems (Webpack, Rollup, Grunt, Gulp).
  • Experience with Agile development practices and tools like Jira.
  • Relevant technical or Agile certifications (e.g., Certified Scrum Master, Microsoft certifications, SAFe).
  • Exposure to AWS services and additional technical certifications.

Kavaliro provides Equal Employment Opportunities to all employees and applicants. All qualified applicants will receive consideration for employment without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state, or local laws. Kavaliro is committed to the full inclusion of all qualified individuals. In keeping with our commitment, Kavaliro will take the steps to assure that people with disabilities are provided reasonable accommodations. Accordingly, if reasonable accommodation is required to fully participate in the job application or interview process, to perform the essential functions of the position, and/or to receive all other benefits and privileges of employment, please respond to this posting to connect with a company representative.

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