About AnoSys
AnoSys is an AI Operational Intelligence Platform for teams running production AI
systems. We connect observability signals, agent traces, evals, cost, user behavior, governance context,
and business outcomes so customers can detect silent regressions, explain non-deterministic behavior,
control spend, and route action to the right owner. Backed by leading investors and built by
infrastructure and AI veterans, AnoSys is expanding AI observability into the operating layer for
agentic software.
Who We're Looking For
We're a small, high-impact team, and every person here shapes the product, the
culture, and the trajectory of the company. We look for intellectually
curious individuals who combine critical thinking with
meticulous attention to detail — people who can identify problems
early, reason through ambiguity, and solve challenges independently.
If you thrive in fast-paced, high-ownership environments — where your work
directly shapes a category-defining product — we'd love to hear from you.
About the Role
As a Solutions Engineer at AnoSys, you will serve as the primary technical interface
between our platform and the engineering organizations evaluating and adopting it. You will work
directly with engineering leaders, platform teams, and individual contributors at companies deploying AI
agents, LLM-powered applications, and ML pipelines in production.
This role sits at the intersection of deep technical expertise and customer empathy.
You'll design end-to-end observability architectures, integrate our SDKs across diverse technology
stacks, and translate complex production challenges into platform capabilities. You will also play a
critical role in shaping the product roadmap by channeling firsthand customer insights back to our
engineering and product teams.
The ideal candidate has a strong background in infrastructure, observability, or
developer tooling and is comfortable operating across pre-sales, onboarding, and post-deployment
workflows. You should be equally at home whiteboarding a distributed tracing architecture with a VP of
Engineering and debugging an OpenTelemetry integration with a senior developer.
What You'll Do
- Partner with prospective and existing customers to deeply understand their observability
requirements for AI agents, LLM pipelines, and ML-powered applications in production environments
- Design, architect, and implement proof-of-concept integrations tailored to each customer's
technology stack, deployment model, and organizational needs
- Deliver compelling technical demonstrations and architecture workshops that clearly articulate how
AnoSys's platform addresses real-world production monitoring challenges
- Serve as the voice of the customer internally — collaborating with Product and Engineering to
translate field learnings into roadmap priorities and feature specifications
- Build and maintain a library of technical documentation, integration guides, reference
architectures, and best-practice resources that accelerate customer onboarding
- Provide technical guidance during the sales process, including RFP responses, security reviews, and
technical due diligence sessions
- Develop reusable tooling, scripts, and automation that streamline common integration patterns across
the customer base
What We're Looking For
- 3+ years in a solutions engineering, sales engineering, or technical consulting role — preferably at
an infrastructure, observability, or developer-tools company (e.g., Datadog, New Relic, Splunk,
Grafana Labs, Honeycomb)
- Strong proficiency in Python and hands-on familiarity with cloud-native architectures across AWS,
GCP, or Azure — including containerized and serverless environments
- Direct experience with observability and monitoring platforms, distributed tracing systems, or
OpenTelemetry-based instrumentation in production settings
- Familiarity with AI/ML development workflows — including model serving, inference pipelines, prompt
engineering, and agent orchestration frameworks (e.g., LangChain, CrewAI, OpenAI Agents)
- Excellent communication and presentation skills — you can lead an architecture review with a CTO,
pair-program with a staff engineer, and present to a non-technical executive in the same day
- A self-directed, entrepreneurial mindset with the ability to manage multiple customer engagements
simultaneously while maintaining a high bar for technical depth and quality
- Intellectual curiosity and a genuine passion for understanding how AI/ML systems behave — and fail —
in production
Nice to Have
- Experience building or contributing to developer SDKs, CLIs, or API client libraries
- Familiarity with data pipeline tools such as Apache Kafka, Google Pub/Sub, or Apache Beam
- Prior experience in an early-stage startup environment where you wore multiple hats and helped
define processes from scratch
- Background in technical writing or developer advocacy