Senior SRE Engineer (MLOps) - AI
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Key skills for this role
About the Role
Salla is looking for a Senior SRE Engineer (MLOps) to join the AI team, focusing on running AI and ML systems as production systems. You will own reliability, observability, safe releases, cost governance, and automation for agentic AI and generative AI features.
Key Skills for This Role
Responsibilities
- Own reliability for ML and agentic AI services in production — SLOs, dashboards, alerts, runbooks, and incident follow ups
- Build observability across the AI stack — latency, errors, traces, tool calls, cost, and user impact
- Design safe release patterns for models, prompts, agents, tools, and configuration, including canary, rollback, feature flag, and evaluation gate strategies
- Provide operational support for inference APIs, queues, retrieval layers, and AI workflows running on Kubernetes/EKS
- Establish ownership, traceability, and guardrails around what agentic systems are allowed to do, including how they call internal tools
- Defend agent tool calling against prompt injection and untrusted data risks — establish and enforce data trust boundaries
- Drive AI cost governance — per model and per pod spend visibility, token cost tracking, and anomaly alerting
- Build automation and self service paths so product teams have a known safe path to production
- Turn recurring operational pain into simple, reusable platform standards that other teams adopt
- Participate in architecture discussions, code reviews, and technical decision making
Requirements
- 4+ years in SRE, platform engineering, DevOps, or production infrastructure, operating distributed systems in production
- Hands on experience with Kubernetes and cloud native systems in production
- Familiarity with deploying ML projects
- Strong command of CI/CD, GitOps, observability, and incident response
- Solid experience with infrastructure as code, secrets management, and networking
- Ability to write automation or platform tooling in Python or a similar language
- Production judgment — knowing how to make systems measurable, debuggable, repeatable, and safe to change
- Ability to work across teams, explain trade offs clearly, and turn operational pain into standards engineers will actually use
Full Job Posting
Role Overview
- Salla is looking for a Senior SRE Engineer (MLOps) to join the Salla AI team.
- This role focuses on running AI and ML systems as real production systems, not side experiments — owning the operational layer around models, prompts, agents, inference services, and retrieval systems.
- You will be responsible for enabling Agentic AI and Generative AI features to operate reliably, securely, and cost effectively at scale within the Salla ecosystem.
- This role is SRE and platform engineering first, with a strong emphasis on reliability, observability, safe releases, cost, and governance.
Key Responsibilities
- Own reliability for ML and agentic AI services in production — SLOs, dashboards, alerts, runbooks, and incident follow ups.
- Build observability across the AI stack — latency, errors, traces, tool calls, cost, and user impact.
- Design safe release patterns for models, prompts, agents, tools, and configuration, including canary, rollback, feature flag, and evaluation gate strategies.
- Provide operational support for inference APIs, queues, retrieval layers, and AI workflows running on Kubernetes/EKS.
- Establish ownership, traceability, and guardrails around what agentic systems are allowed to do, including how they call internal tools.
- Defend agent tool calling against prompt injection and untrusted data risks — establish and enforce data trust boundaries.
- Drive AI cost governance — per model and per pod spend visibility, token cost tracking, and anomaly alerting.
- Build automation and self service paths so product teams have a known safe path to production.
- Turn recurring operational pain into simple, reusable platform standards that other teams adopt.
- Participate in architecture discussions, code reviews, and technical decision making.
Minimum Qualifications
- 4+ years in SRE, platform engineering, DevOps, or production infrastructure, operating distributed systems in production.
- Hands on experience with Kubernetes and cloud native systems in production.
- Familiarity with deploying ML projects.
- Strong command of CI/CD, GitOps, observability, and incident response.
- Solid experience with infrastructure as code, secrets management, and networking.
- Ability to write automation or platform tooling in Python, or a similar language.
- Production judgment — knowing how to make systems measurable, debuggable, repeatable, and safe to change.
- Ability to work across teams, explain trade offs clearly, and turn operational pain into standards engineers will actually use.
Nice to Have
- Experience with MLOps or ML platforms — model serving, registries, evaluation, feature/data dependencies, drift monitoring, or ML pipelines.
- Familiarity with LLM applications or agentic systems — RAG, vector databases, tool calling, workflow orchestration, memory, traces, guardrails, or evaluation pipelines.
- Exposure to tooling such as OpenTelemetry, Prometheus, Grafana, MLflow, KServe, Ray, LiteLLM, vLLM, LangGraph, Arize Phoenix, or LangSmith.
- Experience with Kafka consumers, GPU workloads, inference optimization, model routing, or AI cost governance.
- Experience working in cross functional product teams involving AI, backend, and frontend engineers.
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