Sr IA Engineer

Accountabilities

• • AI-powered developer acceleration: Design and build intelligent tools, internal assistants, and copilots that support code generation, code reviews, testing, documentation, and Infrastructure-as-Code creation.
  • Research and prototyping: Investigate, benchmark, and implement generative AI technologies, including LLMs, RAG pipelines, and AI coding assistants, to improve engineering velocity and reliability.
  • Platform innovation: Develop AI-driven capabilities for cloud and DevOps environments, including cost-aware autoscaling, predictive incident resolution, and automated root-cause analysis.
  • Internal Developer Platform: Work closely with Platform, SRE, and DevOps teams to integrate AI-powered automation into internal engineering platforms and infrastructure workflows.
  • Software delivery optimization: Identify opportunities to reduce manual effort, shorten delivery cycles, improve CI/CD processes, and enhance the overall developer experience.
  • Cross-functional collaboration: Partner with engineering leaders and technical teams to identify operational challenges, test new AI approaches, and translate successful experiments into scalable solutions.
  • Knowledge sharing: Document findings, lead technical workshops, and demonstrate new AI capabilities to help teams adopt effective engineering practices and technologies.

  Requirements

  • Senior-level experience with cloud infrastructure and modern engineering platforms, including Docker, Kubernetes, and AWS or GCP.
  • Strong hands-on experience with Infrastructure-as-Code, particularly Terraform and/or Ansible.
  • Advanced proficiency in Python and scripting languages such as Bash or Go, with the ability to build AI tooling and infrastructure solutions.
  • Practical experience implementing Large Language Models (LLMs) and AI-powered code-generation technologies in real-world software engineering workflows.
  • Strong understanding of the complete Software Development Lifecycle (SDLC), including planning, development, testing, deployment, release, and monitoring.
  • Deep knowledge of DevOps practices and CI/CD automation, with experience applying AI to improve software delivery processes.
  • Research-oriented and curious mindset, with the ability to evaluate emerging technologies, benchmark solutions, and make informed technical recommendations.
  • Strong problem-solving and communication skills, with the ability to explain sophisticated AI concepts and solutions clearly to technical stakeholders.
  • Experience with RAG architectures, prompt engineering, or custom model fine-tuning is an advantage.
  • Experience building or contributing to Internal Developer Platforms (IDPs) is a plus.
  • Open-source contributions, published research, or personal projects applying AI to software quality, deployment safety, or observability are desirable.

  Benefits

  • High-impact opportunity to shape the future of AI-assisted software engineering.
  • High degree of autonomy to research, prototype, and implement emerging GenAI technologies.
  • Collaborative international environment with experienced technical leadership.
  • Opportunity to work at the intersection of AI, cloud infrastructure, DevOps, and software engineering.
  • Long-term engagement with competitive compensation.
  • Professional growth opportunities and exposure to cutting-edge AI technologies.
  • Fully remote working arrangement.
  • Inclusive environment where technical excellence, innovation, and independent thinking are encouraged.