📍 Job Overview

Job Title: Senior AI Cloud Engineer
Company: Custom Software Systems Inc.
Location: Washington, District of Columbia, United States
Job Type: Contractor
Category: AI & Cloud Development
Date Posted: August 3, 2025

🚀 Role Summary

Lead the development and deployment of next-generation AI and analytics solutions using Generative AI techniques and AWS services.
Collaborate with stakeholders to understand business requirements and turn data into actionable insights.
Drive innovation and promote the adoption of new software and technology across the organization.

📝 Enhancement Note: This role requires a strong background in both AI and cloud technologies, with a focus on AWS services. The ideal candidate will have experience in Generative AI, data engineering, and DevSecOps practices.

💻 Primary Responsibilities

AI & Analytics Framework Development: Build and maintain next-generation AI and analytics frameworks using core technologies.
Generative AI Solutions: Utilize Generative AI techniques to create innovative solutions for business challenges.
AWS AI Services: Leverage AWS AI services like Amazon Bedrock, SageMaker, Comprehend, Rekognition, and Transcribe to accelerate AI development and deployment.
Cloud-based Data & AI Solutions: Lead multi-functional teams in designing and implementing cloud-based data and AI solutions.
Data Pipelines & Infrastructure: Ensure data pipelines are scalable, secure, and repeatable, and develop and maintain cloud infrastructure, including data lakes, warehouses, and analytics platforms.
Data Governance & Security: Implement data governance and security best practices, and ensure compliance with relevant regulations and policies.
DevSecOps & CI/CD: Contribute to a DevSecOps culture and automate AI model development, testing, and deployment using CI/CD pipelines.
Training & Collaboration: Conduct training bootcamps and cross-training workshops for internal collaborators and customers, and work on multiple projects as a technical team member driving business requirements end-to-end.

📝 Enhancement Note: This role involves a high level of technical leadership and requires strong problem-solving skills, as well as the ability to work independently and as part of a team.

🎓 Skills & Qualifications

Education: Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, or equivalent work experience.

Experience: At least ten (10+) years of experience in AI, data engineering, and cloud technologies.

Required Skills:

Proven experience in Generative AI, data engineering, AWS AI, and AWS data services.
Proficiency in programming languages such as Python, Java, or similar.
Experience with data engineering concepts and tools.
Experience in Terraform, Docker, Kubernetes, and/or Gitlab.
Understanding of data governance and security principles.
Hands-on experience with DevSecOps and CI/CD practices.
Excellent problem-solving and analytical skills.
Ability to work independently and as part of a team.
Familiarity with government cloud deployment regulations/compliance policies such as FedRAMP, FISMA, etc.
Experience with specific generative AI models like GPT, Llama, Claude, and others from HuggingFace.
Knowledge of deep learning frameworks such as PyTorch and Transformers.

Preferred Certifications:

Certifications in AWS AI or Machine Learning.

📝 Enhancement Note: The ideal candidate will have a strong background in both AI and cloud technologies, with a focus on AWS services. Relevant certifications and experience with specific generative AI models and deep learning frameworks are highly desirable.

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

AI & Analytics Projects: Showcase your experience in developing and deploying AI and analytics solutions using Generative AI techniques and AWS services.
Data Engineering & Cloud Infrastructure: Demonstrate your ability to design, implement, and maintain scalable, secure cloud infrastructure, including data lakes, warehouses, and analytics platforms.
DevSecOps & CI/CD: Highlight your experience in contributing to a DevSecOps culture and automating AI model development, testing, and deployment using CI/CD pipelines.
Training & Collaboration: Provide examples of your ability to conduct training bootcamps and cross-training workshops for internal collaborators and customers.

Technical Documentation:

AI & Analytics Framework Documentation: Document your approach to building and maintaining next-generation AI and analytics frameworks, including the core technologies used.
AWS Services & Infrastructure Documentation: Detail your experience with AWS AI and data services, as well as your approach to designing and implementing cloud-based data and AI solutions.
Data Governance & Security Documentation: Explain your understanding of data governance and security principles, and provide examples of how you have implemented these principles in your projects.
DevSecOps & CI/CD Documentation: Document your experience with DevSecOps and CI/CD practices, and provide examples of how you have automated AI model development, testing, and deployment.

📝 Enhancement Note: Given the technical nature of this role, a strong portfolio demonstrating your experience in AI, data engineering, and cloud technologies is essential. Be prepared to provide detailed documentation and case studies showcasing your technical expertise and problem-solving skills.

💵 Compensation & Benefits

Salary Range: Negotiable

Benefits:

Health insurance plans
Health Savings Account (HSA)
Dental
Vision
Long-term disability
Short-term disability
Basic term life insurance
Supplemental term life insurance for employees, spouses, and dependents
Simple IRA
Parking/Commuting expense reimbursement
Training/Education

📝 Enhancement Note: While the salary range is negotiable, research suggests that senior AI cloud engineers in the Washington, D.C. area with 10+ years of experience can expect to earn between $150,000 and $200,000 per year, depending on factors such as the specific company, industry, and the candidate's skills and experience.

🎯 Team & Company Context

Company Culture:

Industry: Custom Software Systems Inc. operates in the custom software development industry, focusing on providing tailored solutions to meet clients' unique needs.
Company Size: As a medium-sized company, Custom Software Systems Inc. offers a collaborative and innovative work environment, with opportunities for growth and professional development.
Founded: The company was founded in 1995 and has since grown to serve a diverse range of clients across various industries.

Team Structure:

AI & Analytics Team: The AI & Analytics team consists of experienced professionals specializing in AI, machine learning, and data engineering. The team works closely with other departments, such as software development and project management, to deliver cutting-edge solutions to clients.
Reporting Structure: The Senior AI Cloud Engineer will report directly to the Director of AI & Analytics and work closely with other team members, as well as stakeholders from various departments.
Cross-functional Collaboration: The role requires close collaboration with software developers, project managers, and other stakeholders to ensure that AI and analytics solutions meet business requirements and align with overall project goals.

Development Methodology:

Agile/Scrum: The company employs Agile/Scrum methodologies for software development, with a focus on iterative development, continuous improvement, and regular stakeholder involvement.
Code Review & Quality Assurance: The team follows best practices for code review, testing, and quality assurance to ensure the delivery of high-quality, reliable solutions.
Deployment Strategies: The company uses CI/CD pipelines and automated deployment strategies to streamline the release process and ensure consistent, efficient delivery of AI and analytics solutions.

Company Website: Custom Software Systems Inc.

📝 Enhancement Note: Custom Software Systems Inc. places a strong emphasis on innovation, collaboration, and continuous learning. The company's culture encourages employees to stay up-to-date with the latest technologies and industry trends, and to share their knowledge and expertise with their colleagues.

📈 Career & Growth Analysis

AI & Analytics Career Level: The Senior AI Cloud Engineer role is a senior-level position that requires a deep understanding of AI, data engineering, and cloud technologies. The ideal candidate will have extensive experience in developing and deploying AI and analytics solutions, as well as a proven track record of driving innovation and promoting the adoption of new technologies.

Reporting Structure: The Senior AI Cloud Engineer will report directly to the Director of AI & Analytics and will be responsible for leading the AI & Analytics team in designing and implementing cloud-based data and AI solutions. The role requires strong leadership skills, as well as the ability to work collaboratively with other team members and stakeholders.

Technical Impact: The Senior AI Cloud Engineer will play a crucial role in shaping the company's AI and analytics strategy, driving innovation through Generative AI techniques and AWS services. The role requires a strong technical background, as well as the ability to translate complex business needs into actionable AI-driven insights.

Growth Opportunities:

Technical Leadership: The Senior AI Cloud Engineer role offers significant opportunities for technical leadership and growth. The ideal candidate will have experience mentoring team members, driving best practices, and contributing to the development of the company's AI and analytics strategy.
Emerging Technologies: The role provides exposure to emerging technologies and trends in AI and cloud computing, offering opportunities for continuous learning and skill development.
Architecture Decisions: The Senior AI Cloud Engineer will be responsible for making critical architecture decisions that impact the company's AI and analytics infrastructure. This role offers opportunities to develop and refine technical skills, as well as to gain experience in architecture design and implementation.

📝 Enhancement Note: The Senior AI Cloud Engineer role at Custom Software Systems Inc. offers significant opportunities for technical growth and leadership. The ideal candidate will have a strong background in AI, data engineering, and cloud technologies, as well as a proven track record of driving innovation and promoting the adoption of new technologies.

🌐 Work Environment

Office Type: Custom Software Systems Inc. operates a hybrid work environment, with a combination of on-site and remote work options available for eligible employees.

Office Location(s): The company's headquarters is located in Washington, D.C., with additional offices in various locations across the United States.

Workspace Context:

Collaborative Workspace: The company's offices feature collaborative workspaces designed to facilitate teamwork and innovation. Employees have access to shared spaces, meeting rooms, and state-of-the-art technology to support their work.
Development Tools: The company provides employees with access to the latest development tools, multiple monitors, and testing devices to ensure optimal productivity and performance.
Cross-functional Collaboration: The company encourages cross-functional collaboration between teams, with regular meetings and workshops designed to foster knowledge sharing and continuous learning.

Work Schedule: The company offers flexible work arrangements, with core hours between 10:00 AM and 3:00 PM EST. Employees are expected to maintain a consistent work schedule, with a focus on delivering high-quality results rather than strict hours.

📝 Enhancement Note: Custom Software Systems Inc. offers a flexible, collaborative work environment designed to support the needs of its employees and promote innovation and growth. The company's hybrid work model and flexible work arrangements allow employees to balance their professional and personal responsibilities while maintaining a strong connection to the company's mission and values.

📄 Application & Technical Interview Process

Interview Process:

Technical Assessment: Candidates will be required to complete a technical assessment, focusing on their knowledge of AI, data engineering, and cloud technologies. The assessment may include coding challenges, architecture design exercises, and problem-solving scenarios.
Behavioral Interview: Candidates will participate in a behavioral interview, focusing on their problem-solving skills, leadership abilities, and cultural fit with the company. The interview may include questions about their experience with Generative AI, data governance, and DevSecOps practices.
Team Fit & Collaboration: Candidates will have the opportunity to meet with members of the AI & Analytics team and other stakeholders to discuss their approach to collaboration, communication, and teamwork.
Final Evaluation: The final evaluation will focus on the candidate's technical skills, problem-solving abilities, and cultural fit with the company. The evaluation may include a presentation of the candidate's portfolio and a discussion of their approach to AI and analytics solutions.

Portfolio Review Tips:

AI & Analytics Projects: Highlight your experience in developing and deploying AI and analytics solutions using Generative AI techniques and AWS services.
Data Engineering & Cloud Infrastructure: Demonstrate your ability to design, implement, and maintain scalable, secure cloud infrastructure, including data lakes, warehouses, and analytics platforms.
DevSecOps & CI/CD: Showcase your experience in contributing to a DevSecOps culture and automating AI model development, testing, and deployment using CI/CD pipelines.
Training & Collaboration: Provide examples of your ability to conduct training bootcamps and cross-training workshops for internal collaborators and customers.

Technical Challenge Preparation:

AI & Analytics Challenges: Familiarize yourself with the latest trends and best practices in AI and analytics, with a focus on Generative AI techniques and AWS services.
Data Engineering & Cloud Infrastructure Challenges: Brush up on your knowledge of data engineering concepts and tools, as well as cloud infrastructure design and implementation.
DevSecOps & CI/CD Challenges: Review your experience with DevSecOps and CI/CD practices, and be prepared to discuss your approach to automating AI model development, testing, and deployment.
Problem-solving & Communication: Hone your problem-solving skills and be prepared to communicate your technical approach and decisions clearly and effectively.

ATS Keywords: See the comprehensive list of AI & cloud development, data engineering, and DevSecOps-relevant keywords provided at the end of this document.

📝 Enhancement Note: The interview process for the Senior AI Cloud Engineer role at Custom Software Systems Inc. is designed to assess the candidate's technical skills, problem-solving abilities, and cultural fit with the company. Candidates should be prepared to discuss their experience with AI, data engineering, and cloud technologies, as well as their approach to collaboration, communication, and teamwork.

🛠 Technology Stack & Web Infrastructure

AI & Analytics Technologies:

Generative AI: Familiarity with Generative AI techniques, such as GPT, Llama, Claude, and other models from HuggingFace, is required.
Deep Learning Frameworks: Proficiency in deep learning frameworks such as PyTorch and Transformers is expected.
AWS AI Services: Experience with AWS AI services like Amazon Bedrock, SageMaker, Comprehend, Rekognition, and Transcribe is required.
AWS Data Services: Familiarity with AWS data services like Amazon S3, Amazon Redshift, and Amazon DynamoDB is expected.

Cloud Infrastructure & Tools:

Cloud Platforms: Proficiency in cloud platforms such as AWS, Google Cloud Platform, or Microsoft Azure is required.
Infrastructure as Code (IaC): Experience with IaC tools such as Terraform, CloudFormation, or Pulumi is expected.
Containerization & Orchestration: Familiarity with containerization tools like Docker and orchestration platforms like Kubernetes is required.
CI/CD Pipelines: Experience with CI/CD pipelines and tools such as Jenkins, GitLab CI/CD, or CircleCI is expected.

Programming Languages & Tools:

Programming Languages: Proficiency in programming languages such as Python, Java, or similar is required.
Data Engineering Tools: Familiarity with data engineering tools such as Apache Spark, Apache Kafka, or Apache Beam is expected.
Version Control: Experience with version control systems like Git is required.

📝 Enhancement Note: The Senior AI Cloud Engineer role at Custom Software Systems Inc. requires a strong background in AI, data engineering, and cloud technologies. Candidates should be familiar with the latest trends and best practices in Generative AI, AWS services, and cloud infrastructure design and implementation.

👥 Team Culture & Values

AI & Analytics Values:

Innovation: Custom Software Systems Inc. places a strong emphasis on innovation and encourages employees to stay up-to-date with the latest technologies and industry trends.
Collaboration: The company values collaboration and encourages employees to work closely with other teams and stakeholders to deliver cutting-edge solutions.
Continuous Learning: The company fosters a culture of continuous learning and encourages employees to develop their skills and knowledge through training, workshops, and mentorship opportunities.
User-centric Design: The company prioritizes user-centric design and ensures that AI and analytics solutions meet the needs of end-users and drive business value.

Collaboration Style:

Cross-functional Integration: The AI & Analytics team works closely with other teams, such as software development and project management, to ensure that AI and analytics solutions meet business requirements and align with overall project goals.
Code Review Culture: The team follows best practices for code review, testing, and quality assurance to ensure the delivery of high-quality, reliable solutions.
Knowledge Sharing: The company encourages knowledge sharing and provides opportunities for employees to learn from one another through training, workshops, and mentorship programs.

📝 Enhancement Note: Custom Software Systems Inc. fosters a collaborative, innovative, and user-centric culture that values continuous learning and employee growth. The company's AI & Analytics team works closely with other teams and stakeholders to deliver cutting-edge solutions that meet business requirements and drive business value.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Generative AI: Stay up-to-date with the latest trends and best practices in Generative AI, and be prepared to address challenges related to model interpretability, bias, and ethical considerations.
AWS Services: Familiarize yourself with the latest AWS services and features, and be prepared to address challenges related to cost optimization, security, and compliance.
Data Governance & Security: Stay current with data governance and security best practices, and be prepared to address challenges related to data privacy, access control, and incident response.
DevSecOps & CI/CD: Keep up-to-date with the latest trends and best practices in DevSecOps and CI/CD, and be prepared to address challenges related to automation, scalability, and reliability.

Learning & Development Opportunities:

AI & Analytics Training: Custom Software Systems Inc. offers training and development opportunities focused on AI and analytics, including workshops, webinars, and online courses.
Conferences & Events: The company encourages employees to attend industry conferences and events to stay up-to-date with the latest trends and best practices in AI and analytics.
Mentorship & Leadership Development: Custom Software Systems Inc. offers mentorship and leadership development opportunities designed to help employees grow their skills and advance their careers.

📝 Enhancement Note: The Senior AI Cloud Engineer role at Custom Software Systems Inc. presents significant technical challenges and growth opportunities. Candidates should be prepared to address complex business needs, stay up-to-date with the latest trends and best practices in AI, data engineering, and cloud technologies, and embrace a culture of continuous learning and innovation.

💡 Interview Preparation

Technical Questions:

AI & Analytics Fundamentals: Be prepared to discuss your understanding of AI and analytics concepts, as well as your experience with Generative AI techniques, AWS services, and data engineering tools.
Architecture & Design: Demonstrate your ability to design and implement scalable, secure cloud infrastructure, and discuss your approach to architecture decision-making and optimization.
Problem-solving & Communication: Hone your problem-solving skills and be prepared to communicate your technical approach and decisions clearly and effectively.

Company & Culture Questions:

AI & Analytics Culture: Research Custom Software Systems Inc.'s approach to AI and analytics, and be prepared to discuss how your skills and experience align with the company's values and goals.
Collaboration & Teamwork: Demonstrate your ability to work effectively with other teams and stakeholders, and discuss your approach to collaboration, communication, and knowledge sharing.
User-centric Design: Explain your understanding of user-centric design principles and how you have applied them in your previous projects.

Portfolio Presentation Strategy:

AI & Analytics Projects: Highlight your experience in developing and deploying AI and analytics solutions using Generative AI techniques and AWS services.
Data Engineering & Cloud Infrastructure: Demonstrate your ability to design, implement, and maintain scalable, secure cloud infrastructure, including data lakes, warehouses, and analytics platforms.
DevSecOps & CI/CD: Showcase your experience in contributing to a DevSecOps culture and automating AI model development, testing, and deployment using CI/CD pipelines.
Training & Collaboration: Provide examples of your ability to conduct training bootcamps and cross-training workshops for internal collaborators and customers.

📝 Enhancement Note: The interview process for the Senior AI Cloud Engineer role at Custom Software Systems Inc. is designed to assess the candidate's technical skills, problem-solving abilities, and cultural fit with the company. Candidates should be prepared to discuss their experience with AI, data engineering, and cloud technologies, as well as their approach to collaboration, communication, and teamwork.

📌 Application Steps

To apply for this Senior AI Cloud Engineer position at Custom Software Systems Inc.:

Tailor Your Resume: Customize your resume to highlight your experience with AI, data engineering, and cloud technologies, as well as your relevant skills and qualifications.
Prepare Your Portfolio: Curate a portfolio of your AI and analytics projects, demonstrating your ability to develop and deploy solutions using Generative AI techniques and AWS services.
Research the Company: Familiarize yourself with Custom Software Systems Inc.'s approach to AI and analytics, as well as the company's values and culture.
Prepare for Technical Challenges: Brush up on your knowledge of AI, data engineering, and cloud technologies, and be prepared to address technical challenges related to Generative AI, AWS services, and data governance and security.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and web development/server administration industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.

Comprehensive List of AI & Cloud Development, Data Engineering, and DevSecOps-relevant Keywords

Programming Languages:

Python
Java
R
Scala
JavaScript
TypeScript
Go
C++
Swift

Web Frameworks & Libraries:

TensorFlow
PyTorch
Keras
Scikit-learn
HuggingFace
AWS SDK
AWS Amplify
AWS AppSync
AWS Lambda
AWS DynamoDB
AWS S3
AWS Redshift
AWS Glue
AWS Athena
AWS QuickSight
AWS SageMaker
AWS Comprehend
AWS Rekognition
AWS Transcribe
AWS Textract
AWS Bedrock
AWS DeepRacer
AWS DeepComposer
AWS DeepIntent
AWS Personalize
AWS Lookout
AWS Shield
AWS WAF
AWS IAM
AWS Cognito
AWS Key Management Service (KMS)
AWS Certificate Manager
AWS CloudFormation
AWS CloudFront
AWS Route 53
AWS Direct Connect
AWS Transit Gateway
AWS VPN
AWS Direct Connect
AWS Outposts
AWS Local Zones
AWS Wavelength
AWS Elastic Beanstalk
AWS ECS
AWS EKS
AWS Fargate
AWS Batch
AWS Step Functions
AWS EventBridge
AWS CloudWatch
AWS CloudTrail
AWS Config
AWS Trusted Advisor
AWS Well-Architected
AWS Well-Architected Framework
AWS Well-Architected Review
AWS Well-Architected Lenses
AWS Well-Architected Partner Program
AWS Well-Architected Research
AWS Well-Architected Labs
AWS Well-Architected Training
AWS Well-Architected Certification
AWS Certified Cloud Practitioner
AWS Certified Solutions Architect
AWS Certified Developer
AWS Certified Advanced Networking Specialty
AWS Certified Security Specialty
AWS Certified Big Data Specialty
AWS Certified Alexa Specialty
AWS Certified Machine Learning Specialty
AWS Certified Data Analytics Specialty
AWS Certified IoT Specialty
AWS Certified Serverless Specialty
AWS Certified CloudFront Specialty
AWS Certified Alexa Skill Builder - Specialty
AWS Certified Alexa Conversations Specialty
AWS Certified Alexa AutoML Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
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AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
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AWS Certified Alexa AutoML - Specialty
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AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty
AWS Certified Alexa AutoML - Specialty

Senior AI Cloud Engineer