Company Description Klimber Technologies is an AI-focused company built on more than two decades of enterprise data and analytics experience and deep cloud expertise across AWS, GCP, and Azure. The team has a strong track record of delivering production-grade solutions for Fortune 100 organizations, with a transparent conviction that technology should remove friction and solve real problems. Klimber’s products, including Turn3 and hypernear, focus on AI observability and retrieval infrastructure at true production scale, enabling reliable multi-turn agents, RAG pipelines, semantic search, and robust agent memory.

The company’s vision is a future where enterprises can move quickly with AI without sacrificing trust, with every pilot having a practical path to production. Beyond its products, Klimber partners with enterprises and software vendors to deliver end-to-end LLMOps, domain-customized language models, and human-in-the-loop moderation for regulated, high-stakes use cases.

Role Description The AI Observability Engineer will design, build, and maintain observability solutions for AI systems, with a focus on multi-turn agents, LLM-based applications, and retrieval-augmented workflows. In this remote contract role, responsibilities include instrumenting AI pipelines, defining and implementing metrics, logs, and traces, and creating dashboards and alerting for model performance, reliability, and user outcomes. The engineer will reconstruct and analyze full agent sessions, identify failure patterns,



and convert them into safeguards and improvements across Turn3, hypernear, and customer environments.

Day-to-day work involves collaborating with product, data, and platform teams to integrate observability into production deployments, optimizing infrastructure across clouds, and contributing to internal best practices for AI safety, quality, and monitoring. The role also includes documenting systems, participating in code reviews, and helping customers operationalize AI observability in complex enterprise contexts.

Qualifications

- Strong foundations in Computer Science and Software Development, including data structures, algorithms, APIs, and production-grade systems.
- Experience with Neural Networks and Natural Language Processing (NLP), particularly in building, deploying, or monitoring LLMs and agent-based solutions.
- Ability to apply Pattern Recognition to identify behavioral trends, model failures, and systemic issues in AI applications.
- Hands-on experience with observability tools and practices (e.g., Prometheus, OpenTelemetry, Grafana, ELK, tracing and logging frameworks) in distributed systems.
- Proficiency with at least one major cloud platform (AWS, GCP, or Azure) and modern DevOps practices (containers, CI/CD, infrastructure-as-code).
- Experience working with vector databases, similarity search, or RAG pipelines, and understanding of their performance and reliability considerations.
- Comfort analyzing production data and user sessions

📌 AI Observability Engineer (India)
🏢 Klimber Technologies
📍 India