Senior SRE Engineer (MLOps) - AI
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Key skills for this role
About the Role
Salla is seeking a Senior SRE Engineer (MLOps) to join the AI team, focusing on operational reliability for AI/ML systems including agentic AI and generative AI features. The role involves owning reliability, observability, safe-release patterns, and cost governance for production AI services.
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
- Provide operational support for inference APIs, queues, retrieval layers, and AI workflows on Kubernetes/EKS
- Establish ownership, traceability, and guardrails for agentic systems
- Defend agent tool calling against prompt injection and untrusted data risks
- Drive AI cost governance — per model and per pod spend visibility, token cost tracking, and anomaly alerting
- Build automation and self service paths for product teams
- Turn recurring operational pain into reusable platform standards
- 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 similar language
- Production judgment for measurable, debuggable, repeatable, and safe systems
- Ability to work across teams, explain trade offs, and turn operational pain into standards
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, owning the operational layer around models, prompts, agents, inference services, and retrieval systems.
- The role is SRE and platform engineering first, with 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.
- Defend agent tool calling against prompt injection and untrusted data risks.
- 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.
- 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.
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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