Member of Technical Staff

THE ROLE

Everpure is seeking a talented and experienced Agentic AI Software Engineer to join our dynamic team in Santa Clara, CA. In this role, you will build production-grade agentic AI solutions that drive enterprise automation and digital transformation. You will design and deploy LLM-based services , implement strong prompt engineering techniques, guardrails, and model evaluation frameworks, and ensure secure, scalable delivery on AWS. This role focuses on building reliable, secure, and observable AI systems integrated with enterprise platforms.

WHAT YOU'LL DO

• Design and develop production-grade Agentic AI solutions using LLMs, multi-agent workflows, and RAG pipelines.
• Implement and refine Prompt Engineering strategies, including structured prompts, tool-calling, and agent orchestration to ensure reliable and deterministic AI behavior.
• Build secure backend services and REST APIs in Python, integrating AI agents with enterprise systems using microservices architecture.
• Establish robust guardrails, including output validation, fallback strategies, rate limiting, safe tool invocation, and human-in-the-loop patterns where appropriate.
• Develop and maintain model evaluation frameworks, including automated prompt testing, retrieval validation, regression testing, and performance benchmarking.
• Deploy and operate AI services on AWS, leveraging services such as EC2, S3, Lambda, RDS, and containerization using Docker and Kubernetes.
• Apply strong security best practices, including access control, data protection, audit logging, and secure API design.
• Ensure strong observability, monitoring, and reliability of AI-driven services in production.
• We are primarily an in-office environment and therefore, you will be expected to work from the Santa Clara, CA office in compliance with Everpure's policies, unless you are on PTO, or work travel, or other approved leave.

WHAT YOU BRING

• 3–6 years of professional software engineering experience building backend or distributed systems.
• Strong proficiency in Python and developing RESTful APIs.
• Hands-on experience building LLM-based or Agentic AI applications, including RAG, embeddings, and vector database integrations.
• Practical experience in Prompt Engineering and understanding of LLM behavior, limitations, and optimization techniques.
• Experience deploying and operating applications in AWS cloud environments.
• Experience implementing AI guardrails, model validation techniques, and production monitoring.
• Solid understanding of cloud-native security practices and secure system design.

Preferred Qualifications

• Experience with multi-agent orchestration frameworks or AI tool integration patterns.
• Familiarity with vector databases such as Pinecone, Weaviate, OpenSearch, or similar.
• Experience with CI/CD pipelines and containerized deployments using Docker and Kubernetes.
• Exposure to enterprise platform integrations such as ServiceNow, Snowflake, or similar systems.
• Familiarity with observability tools such as Datadog, Prometheus, or similar platforms.

 

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