Senior Cloud Software Engineer (AWS/AI)

Remote from: USA
Salary, yearly, USD: 150,000 - 150,000
Department: Software Engineering
Employment type: Full Time,
Job posted: 9 May 2026
Apply before: 9 Jun 2026
Experience level: Senior
Views / Applies: 799 / 42

About ECS

Partnering with leading organizations to deliver powerful solutions for a complex world.

Actively Hiring

AI Summary

Everforth ECS is hiring a Senior Cloud Software Engineer with AI/ML expertise for a hybrid role in Memphis, TN or fully remote. The position involves designing and deploying scalable AWS solutions, modernizing analytical workflows, and embedding ML into critical systems. The ideal candidate is a versatile technologist skilled in full-stack development, cloud infrastructure, and AI integration, capable of mentoring teams and solving complex challenges. Key responsibilities include leading full-stack development, architecting AI/ML pipelines, and managing cloud deployments. The role offers a salary of $150,000 per year plus benefits.

Job Complexity

Easy Hard

AI Insight The role requires deep expertise in full-stack development, cloud infrastructure, and AI/ML integration, along with leadership and mentoring skills. The broad technical requirements and fast-paced environment make it challenging, but not the hardest level.

Salary Analysis

AI Insight The offered salary of $150,000 is competitive for a senior cloud engineer with AI/ML expertise, falling near the median for such roles in the US market. The market range for similar positions typically spans $130,000 to $190,000, so this offer is solidly within expectations.

Key Skills

AWS AI/ML Full-Stack Development Java/C++ React NodeJS Kafka RESTful APIs DevOps Infrastructure as Code

Cover Letter Sample

I am writing to express my strong interest in the Senior Cloud Software Engineer (AWS/AI) position at Everforth ECS. With extensive experience in full-stack development and cloud-native architectures, I have successfully led the design and deployment of scalable solutions on AWS, integrating AI/ML pipelines to drive actionable insights. My background includes building RESTful APIs, modern JavaScript interfaces, and managing high-throughput systems using Kafka and other distributed technologies.

I thrive in fast-paced environments and am adept at rapidly adapting to new tools to solve complex challenges. I have a proven track record of mentoring team members and translating technical concepts for diverse audiences, ensuring alignment with business goals. I am particularly excited about the opportunity to modernize analytical workflows and embed machine learning into mission-critical systems at Everforth ECS.

I am confident that my technical expertise and collaborative approach would make me a valuable asset to your team. I look forward to the possibility of discussing how I can contribute to your innovative projects.

Possible Interview Questions

Describe a time you led the design and deployment of a scalable full-stack application on AWS. What challenges did you face and how did you overcome them?

I led the migration of a legacy monolithic application to a microservices architecture on AWS using ECS and Lambda. Challenges included data consistency and service discovery. We implemented event-driven communication with Kafka and used API Gateway for routing, ensuring scalability and reliability.

How do you approach integrating machine learning models into production systems? Can you give an example?

I typically start by containerizing the model using Docker and deploying it as a microservice on AWS SageMaker or ECS. For a fraud detection system, I integrated a real-time inference endpoint with Kafka streams, enabling low-latency predictions while maintaining model versioning and monitoring.

Explain your experience with modern JavaScript frameworks and how you ensure responsive and performant user interfaces.

I have extensive experience with React and Angular. I focus on component-based architecture, state management with Redux, and lazy loading to optimize performance. For a dashboard application, I implemented virtual scrolling and memoization to handle large datasets efficiently.

How do you handle mentoring junior developers while managing your own technical responsibilities?

I prioritize by setting aside dedicated time for code reviews and pair programming sessions. I also create documentation and conduct knowledge-sharing sessions on AWS best practices and AI/ML integration. This ensures team growth without compromising project timelines.

Describe a situation where you had to troubleshoot a complex system-level issue in a cloud environment. What was your approach?

Once, a production service had intermittent latency spikes. I used AWS CloudWatch and X-Ray to trace requests, identified a bottleneck in database queries due to missing indexes, and optimized the queries. I also implemented caching with ElastiCache, reducing latency by 70%.

Everforth ECS is seeking a Senior Cloud Software Engineer (with AI/ML) expertise to work hybrid in our Memphis, TN office or fully remote.

ECS is seeking a Senior Cloud Software Engineer with deep expertise in full-stack development, cloud infrastructure, and AI/ML integration. This role involves designing and deploying intelligent, scalable solutions in AWS environments, with a strong emphasis on modernizing analytical workflows and embedding machine learning capabilities into mission-critical systems.

The ideal candidate is a versatile technologist who thrives in fast-paced environments, rapidly adapts to emerging tools and platforms, and consistently delivers innovative, data-driven solutions. You’ll play a key role in transitioning legacy services to the cloud, collaborating with a team of developers and architects to build resilient platforms that support advanced analytics, real-time processing, and intelligent automation.

You should be able to apply scientific rigor, business insight, and engineering best practices to solve complex customer challenges. This includes evaluating data accuracy, deploying ML models, and building systems that extract actionable insights. You’re an expert in computer science, engineering, or business, and capable of mentoring others while sharing your cloud and AI/ML expertise across teams and contracts.

Key Responsibilities

Lead the design, development, and deployment of full-stack software solutions, including RESTful APIs, modern JavaScript-based user interfaces, and scalable cloud-native architectures
Architect and implement complex analytical workflows and AI/ML pipelines, integrating models into production systems for AWS services
Define and manage data exchange processes between internal and external systems, ensuring secure, high-throughput communication and interoperability
Apply deep expertise in software engineering to troubleshoot and resolve intricate system-level issues, using best practices in performance tuning, testing, and code reviews
Rapidly prototype intelligent applications and analytical tools to support dynamic client initiatives, leveraging infrastructure-as-code and DevOps automation
Evaluate cloud infrastructure and data systems for accuracy, reliability, and cost-efficiency, optimizing deployments across hybrid environments
Communicate complex technical concepts clearly across technical, managerial, and customer audiences, translating requirements into actionable solutions
Research and analyze customer needs to deliver tailored, scalable, and secure cloud applications with embedded machine learning capabilities
Monitor and manage deployed environments, ensuring operational stability, security, and performance
Mentor and train team members, sharing expertise in cloud engineering, software development, and AI/ML integration across organizational boundaries

Salary Range $150,000

General Description of Benefits

Qualifications

Proven expertise in full-stack software development, with a strong foundation in Java or C++
Hands-on experience with modern JavaScript frameworks such as React, and proficiency in NodeJS and Java toolchains (NPM, Angular CLI, Maven)
Practical knowledge of deploying and managing applications on AWS using cloud-native methodologies
Solid understanding of core web technologies including HTML, CSS, and JSON
Ability to architect, build, and maintain scalable full-stack applications in cloud and hybrid environments
Experience developing analytical workflows and intelligent data pipelines to support client initiatives
Skilled in designing and implementing responsive user interfaces using popular JavaScript frameworks
Proficient in building secure, production-grade RESTful web services and APIs
Experience deploying and managing distributed messaging systems, particularly Kafka
Capable of monitoring, tuning, and maintaining cloud-based environments for performance and reliability
Strong background in designing systems that collect, store, and process large-scale data for actionable insights, including AI/ML model integration
Comfortable working within agile development teams and contributing to iterative delivery cycles

Technical Skills & Tools Experience

Cloud Platforms: AWS (EC2, Lambda, SageMaker, EMR, S3, DynamoDB, CloudFormation)
AI/ML Frameworks: TensorFlow, PyTorch, Scikit-learn, AWS SageMaker
Front-End Development: React, Angular, Svelte, TypeScript, HTML/CSS
Back-End & APIs: Java, Node.js, Flask, REST, GraphQL
Containerization & Orchestration: Docker, Kubernetes, Rancher
Data Engineering & Stores: PostgreSQL, MySQL, MongoDB, Redis, Kafka, JSON
Operating Systems: Linux, Bash scripting
Security & Identity: Keycloak, SAML, OAuth2, vulnerability mitigation
DevOps & Automation: GitLab CI/CD, Jenkins, Ansible, AWS CDK, Terraform
Project & Source Management: GitHub, GitLab, Version One
Development Methodologies: Agile, Scrum, Rapid Prototyping

Minimum Qualifications

Master’s Degree in Computer Science, Engineering, Mathematics, Business Management or equivalent.
US Citizen or Green Card Holder (must be eligible to obtain a Public Trust Clearance)
12+ years of general experience in information systems, including at least 8+ years of specialized experience in cloud engineering and full-stack software development
Demonstrated success in collaborating with clients to gather requirements and deliver scalable AI/ML-enabled solutions
Hands-on experience deploying machine learning models and data pipelines in cloud environments (preferably AWS)
Strong understanding of software development lifecycle and agile methodologies, with emphasis on data-driven and intelligent applications

Apply now >