Ai Back End Engineer (Llm Specialist)

Senior AI Platform Backend Engineer (LLM): Designing and implementing cloud infrastructure and backend architecture for AI Verification and Az ChatGPT Services with an accent on MLOps, LLM-as-a-judge production services, and high-load RAG solutions. Focus on optimizing model latency, prompt engineering, and evaluating agent-based solutions for AI-generated content validation.Location:Hybrid in Barcelona, Spainwith remote-work options.CompanyNEORIS, now part of EPAM, is a Digital Accelerator with over 20 years of experience, fostering a multicultural and startup-driven culture to create high-impact solutions for clients globally.What you will doDesign, implement, and manage cloud infrastructure on AWS using Infrastructure as Code (IaC) tools like Terraform.Maintain and improve CI/CD pipelines using tools such as GitHub Actions, AWS CodePipeline, Jenkins, or ArgoCD.Develop backend architecture for AI Verification and Az ChatGPT Services using Python and FastAPI.Develop and maintain LLM-as-a-judge production services for validating AI-generated content, optimizing model latency.Design, build, and maintain high-load RAG solutions including ingestion and retrieval services.Perform prompt engineering using techniques like Chain-of-Thought across multiple LLMs.Requirements5+ years of experience designing and implementing cloud solutions in AWS (Azure is a plus).Proven background in building solutions involving IaaS, PaaS, Serverless, and distributed architectures.Advanced knowledge of Kubernetes, microservices, and IaC (Terraform).Hands-on experience with Machine Learning pipelines, MLOps, and LLM-based solutions (Chatbots, RAG).Strong experience developing in Python, including FastAPI.Experience with observability tools (Prometheus, Grafana, ELK, OpenTelemetry) and CI/CD/DevOps practices.High level of English (C1 or equivalent).Nice to haveExperience with Cloud Security and compliance.Familiarity with cloud cost-optimization tools and automation frameworks.Culture & BenefitsPermanent contract with a competitive salary.Flexible work model with remote-work options.Personalized career plan and continuous training (certifications, English, etc.).Participation in stable projects with strong technical components.Flexible schedule with focus on work-life balance.Social benefits tailored to your needs.#J-*****-Ljbffr

5+ years of experience designing and implementing cloud solutions in AWS (Azure is a plus) . Proven background in building solutions involving IaaS, PaaS, Serverless, and distributed architectures. Advanced knowledge of Kubernetes, microservices, and IaC (Terraform). Hands-on experience with Machine Learning pipelines, MLOps, and LLM-based solutions (Chatbots, RAG). Strong experience developing in Python, including FastAPI. Experience with observability tools (Prometheus, Grafana, ELK, OpenTelemetry) and CI/CD/DevOps practices. High level of English (C1 or equivalent) . Nice to have Experience with Cloud Security and compliance. Familiarity with cloud cost-optimization tools and automation frameworks.

Permanent contract with a competitive salary. Flexible work model with remote-work options. Personalized career plan and continuous training (certifications, English, etc.). Participation in stable projects with strong technical components. Flexible schedule with focus on work-life balance. Social benefits tailored to your needs. #J-*****-Ljbffr

NEORIS, now part of EPAM, is a Digital Accelerator with over 20 years of experience, fostering a multicultural and startup-driven culture to create high-impact solutions for clients globally. What you will do Design, implement, and manage cloud infrastructure on AWS using Infrastructure as Code (IaC) tools like Terraform. Maintain and improve CI/CD pipelines using tools such as GitHub Actions, AWS CodePipeline, Jenkins, or ArgoCD. Develop backend architecture for AI Verification and Az ChatGPT Services using Python and FastAPI. Develop and maintain LLM-as-a-judge production services for validating AI-generated content, optimizing model latency. Design, build, and maintain high-load RAG solutions including ingestion and retrieval services.