**.

Responsibilitie**

• s:Design, build, and maintain scalable, high-performance backend services and infrastructure for Fetcherr's LLM ecosyste
• m.Develop and manage APIs and microservices that facilitate the interaction between LLM models, data pipelines, and end-user application
• s.Implement and optimize data pipelines for ingesting, processing, and serving data relevant to LLM training and inferenc
• e.Ensure the reliability, security, and efficiency of LLM deployment environments through robust infrastructure managemen
• t.Collaborate with LLM Engineers and Data Scientists to understand their infrastructure needs and provide tailored solution
• s.Establish and maintain best practices for Python development, including coding standards, testing (unit, integration, E2E), reproducibility, and version control for infrastructure cod
• e.Leverage cloud platforms (e.g., GCP) to build and manage scalable infrastructure, including compute, storage, and networking resource
• s.Implement monitoring, logging, and alerting systems to ensure the health and performance of LLM services and infrastructur
• e.Contribute to the architectural decisions and strategic direction for Fetcherr's AI infrastructur
• e.Stay abreast of industry trends and best practices in Python development, cloud computing, and MLOp

**s.

You'll be a great fit if you have**

• ...5+ years of professional experience in backend software development, with a strong emphasis on Pyth
• on.Proven experience in building and managing production systems and scalable infrastructu
• re.Strong understanding of building applications that scale and operate reliably in a cloud environme
• nt.Expertise in designing and implementing APIs and microservic
• es.Solid experience with cloud platforms, particularly GCP (preferred), including services for compute, storage, networking, and managed databas
• es.Demonstrated experience with good coding practices for testing, reproducibility, and version control (e.g., Gi
• t).Familiarity with containerization technologies such as Docker and orchestration platforms like Kubernet
• es.Experience with CI/CD pipelines and tools for automated testing and deployme
• nt.Proficiency in database technologies (SQL and NoSQ
• L).Excellent problem-solving, analytical, and debugging skil
• ls.Strong communication and collaboration skills, with the ability to articulate technical concepts effective

ly.Nice to ha

• ve:Hands-on experience with MLOps practices and tools (e.g., Kubeflow, MLflow, TensorFlow Extende
• d).Experience with serving LLMs, including model optimization techniques and efficient inferen
• ce.Knowledge of LLM frameworks and libraries (e.g., LangChain, Transformer
• s).Experience with data streaming technologies (e.g., Kafka, Pub/Su
• b).Understanding of security best practices for cloud infrastructure and applicatio
• ns.Familiarity with infrastructure-as-code tools (e.g., Terraform, CloudFormatio

n).