DevOps and GenAI Backend Developer

Gamma Technologies-posted about 2 months ago
$110,000 - $120,000/Yr
Full-time • Mid Level
Hybrid • Westmont, IL
51-100 employees
Publishing Industries
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GT, a leading multi-physics CAE simulation software provider, develops a suite of integrated solutions that guides and accelerates the engineering transformation of today's products in the transportation, power generation, and industrial equipment industries. At Gamma Technologies, our people are the driving force behind our success. We are looking for DevOps and GenAI Backend Developer who shares our passion for authentic innovation, trusted partnerships, bold decisions and a relentless focus on customer success. You'll be at the forefront of blending cloud infrastructure and AI innovation by designing, implementing, and maintaining the backbone that allows our Generative AI services to operate reliably and scale effortlessly. You will also contribute DevOps expertise in areas outside of GenAI, such as our next generation modeling environment GT-Play and on-demand cloud computing services.

  • Perform DevOps activities including cloud architecture definition, deployments, CI/CD pipelines enhancement, maintenance and system monitoring for Generative AI applications, and other cloud-based software.
  • Create, maintain, and update architecture as code and pipeline scripts used to deploy products and their cloud architecture.
  • Operate a wide range of cloud services deployed on Amazon Web Services (i.e. Amazon Bedrock, Amazon Cognito, EC2, ECS, SES, ELB)
  • Work on hardening security of cloud-based environment (architecture and software) to match compliance with strong information security standards (like TISAX or ISO 27001)
  • Troubleshoot incidents and coordinate with developers of products involved to provide support and fixes.
  • BS/MS in Computer Science, Engineering, AI, or related field / equivalent professional experience.
  • AWS Cloud Architecture: Strong hands-on experience in managing and deploying web applications and cloud architecture on AWS. Ability to create Cloud Architecture for a given web-based product from scratch with focus on auto-scaling, load balancing, data migration and cost optimization.
  • AI Applications: Proven track record of deploying and maintaining Machine Learning (ML) / Large Language Model (LLM) applications in production environment with active users.
  • DevOps background: Minimum of 3 years' experience and proficiency in Python for automating pipelines, integrating AI workflows, and optimizing deployments.
  • Secure Infrastructure Practices: Comfortable operating within environments that enforce strict access controls and compliance-driven workflows. Contribute to ensure systems remain resilient and protected.
  • Collaboration: Experience working closely with developers to manage staging environments, troubleshoot and debug with them to understand the inner workings of various products.
  • Technical Skills and Tools:
  • AI Awareness: Basic understanding of Generative AI concepts (RAG, agents, data processing, prompt engineering); ability to work with engineers on prompt and pipeline improvements. Knowledge of GPU infrastructure management, token usage optimization, and scaling strategies for API-based LLMs
  • Automated Deployment Tools: Proficiency with tools dedicated to automated deployment like CloudFormation Templates / Terraform / Kubernetes yaml files / Ansible. (Preferred tool: CloudFormation Templates, Ansible)
  • Database: Can manage structured/unstructured data on platforms such as DynamoDB, PostgreSQL, or similar
  • Version Control Systems: Experience with Git or Perforce code repositories. (Preferred tool: GitLab)
  • Coding/Knowledge of the stack: good knowledge of Java, Python and web frontend frameworks (Angular), and understand how to read and analyze stack trace for these
  • U.S. citizenship is required for this position, as the selected candidate will need to obtain and maintain a U.S. Department of Defense security clearance.
  • Agents: Basic Familiarity with frameworks like CrewAI, LangChain, or Model Context Protocol (MCP) for agent orchestration and retrieval optimization
  • Cost Optimization: Knowledge of cost optimization strategies for large-scale AI workloads (spot instances, batching, inference caching)
  • Security: Experience securing AI pipelines and models against adversarial inputs or data leakage
  • Competitive total rewards program with health and financial benefits (401K and profit sharing).
  • Flexible work options. This role will require you to be in the office 60% of the time.
  • Generous vacation, sick days, holidays and including parental leave.
  • Onsite fitness center.
  • Growth and development opportunities.
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