{"id":2503696,"url":"https://alion.io/job/firebird-mlops-engineer-ml-platform-and-delivery","title":"MLOps Engineer, ML Platform and Delivery","company":{"id":2031237,"name":"Firebird","domain":"firebird.ai","url":"https://alion.io/company/firebird-ai","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Spark","truth_index":{"grade":"C","score":58,"open_postings":13,"ghost_share":0.692,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-10-15T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":null,"employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Yerevan, Armenia"],"countries":["AM"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Alertmanager","optional":false},{"name":"Apache Kafka","optional":false},{"name":"CI/CD","optional":false},{"name":"ClickHouse","optional":false},{"name":"CUDA","optional":false},{"name":"CUDA Toolkit","optional":false},{"name":"Docker","optional":false},{"name":"GitHub Actions","optional":false},{"name":"Grafana","optional":false},{"name":"Helm","optional":false},{"name":"KEDA","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"PostgreSQL","optional":false},{"name":"Prometheus","optional":false},{"name":"Python","optional":false},{"name":"Redis","optional":false},{"name":"Terraform","optional":false},{"name":"ArgoCD","optional":true},{"name":"AWS","optional":true},{"name":"Azure","optional":true},{"name":"Envoy","optional":true},{"name":"FinOps","optional":true},{"name":"Gateway API","optional":true},{"name":"GCP","optional":true},{"name":"GitOps","optional":true},{"name":"InfiniBand","optional":true},{"name":"Kubeflow","optional":true},{"name":"Metaflow","optional":true},{"name":"MLFlow","optional":true},{"name":"NVLink","optional":true}],"status":"live","first_seen_at":"2026-09-21T00:00:00Z","employer_posted_date":"2026-09-21","last_verified_at":"2026-10-15T13:52:42Z","board_verified":true,"closed_at":null,"days_open":24,"trust":{"level":"ok","repost_count":0,"flags":["company_stale"],"days_open":24},"description":"About the Role\nWe are looking for an experienced MLOps Engineer to build and operate the infrastructure that powers Firebird AI’s production machine learning and LLM workloads.\nIn this role, you will own the deployment, reliability, scalability, and observability of GPU-based AI services across Kubernetes environments. You will work at the intersection of machine learning, infrastructure, and platform engineering, building the systems that enable ML and product teams to reliably deploy and operate models at scale.\nYou will work closely with ML Engineering, Platform, and Infrastructure teams to improve GPU utilization, automate deployments, optimize inference infrastructure, and ensure production AI services remain reliable and performant.\nWhat You’ll Do\nBuild, operate, and continuously improve Kubernetes-based infrastructure for production GPU and machine learning workloads.\nDesign and maintain scalable deployment patterns for LLM inference and other AI services.\nOwn CI/CD pipelines, deployment automation, Helm charts, and infrastructure configuration for ML services.\nDesign observability across model serving, gateway, infrastructure, and pipeline layers using Prometheus, Grafana, Alertmanager, and related tooling.\nDefine and maintain production metrics, SLOs, dashboards, and alerting strategies.\nOptimize workload scheduling, GPU utilization, autoscaling, traffic routing, batching, and resource allocation.\nBuild and operate stateful services supporting the ML platform, including Kafka, Redis, ClickHouse, and PostgreSQL.\nImplement reliable traffic management strategies, including load balancing, canary deployments, shadow traffic, and cache-aware routing.\nImprove container build and deployment performance, including image size, cold-start time, CUDA dependencies, and reproducibility.\nParticipate in production on-call, lead incident response, perform root cause analysis, and implement long-term corrective actions.\nCollaborate with ML engineers on productionizing models and translating serving requirements into reliable infrastructure.\nContinuously evaluate emerging technologies across GPU infrastructure, Kubernetes, LLM serving, and ML platform engineering.\nWhat We’re Looking For\n3+ years MLOps, ML platform, DevOps or SRE, with 2+ running GPU workloads in production.\nKubernetes with GPU specifics: device plugins, node selectors and taints, pool shapes, resource requests and limits, autoscaling (HPA/KEDA) including on engine queue depth.\nHelm chart authoring and customize as the primary tooling; Terraform or equivalent a plus.\nCI/CD ownership with GitHub Actions on self-hosted runners; workflows that call idempotent scripts, static validation gates on every PR.\nObservability designed, not inherited: Prometheus, Grafana, Alertmanager; defining metrics, SLOs and alert rules for serving, gateway and pipeline tiers.\nOperating stateful services on Kubernetes: Kafka, Redis, ClickHouse, PostgreSQL, from single-node to a production topology with backups and a restore drill.\nTraffic routing and serving optimization - request routing across replicas, prefix/KV-cache-aware routing, load balancing, batching policy, autoscaling on queue depth, canary and shadow traffic splitting\nDocker depth: multi-stage builds, pinned upstream tags, image size, cold-start control, CUDA base images\nProduction on-call track record: owned incidents, wrote postmortems, shipped the fixes.\nStrong shell and working Python.\nNice to Have\nBare-metal or on-prem GPU cluster bring-up: GPU operator, driver lifecycle, NVLink and InfiniBand, local registry and object storage.\nMulti-node inference orchestration and its networking.\nCloud GPU infrastructure (Nebius, AWS, GCP or Azure): instance selection, quota and capacity management.\nEnvoy Gateway or Envoy proxy and Gateway API: routes, policies, access-log telemetry.\nNVIDIA Dynamo operator: disaggregated prefill/decode, planner and worker configuration.\nAIPerf or GenAI-Perf for load generation in CI.\nCanary and shadow traffic splitting at the gateway.\nSecrets management, RBAC, NetworkPolicy, image signing and scanning, supply-chain security.\nExperiment and model tracking: MLflow, Kubeflow or Metaflow.\nLLM-aware request routing operated in production: at least one of Gateway API Inference Extension (InferencePool/EPP), llm-d, NVIDIA Dynamo.\nGPU cost attribution and rightsizing (FinOps).\nArgo CD or Flux, if GitOps reconciliation replaces manual deploys.\nMIG or time-slicing.","description_format":"text","description_chars":4457,"description_truncated":false,"requirements":{"experience_years_min":null,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[],"hiring_excludes":[],"relocation_offered":false,"industries":["Data Centers & Colocation","AI Compute & Inference"],"lifecycle":[{"event":"open","at":"2026-10-14T06:59:58Z"}],"visa":[],"liveness":{"score":23,"band":"cold","label":"Long shot","p_open":1,"p_active":0.422,"p_room":0.55,"age_days":24,"expected_fill_days":21,"reasons":["conf:3","stale_co","win:tail"],"computed_at":"2026-10-15T05:46:00Z"},"pay":null,"html_url":"https://alion.io/job/firebird-mlops-engineer-ml-platform-and-delivery","json_url":"https://alion.io/job/firebird-mlops-engineer-ml-platform-and-delivery.json","meta":{"generated_at":"2026-10-15T17:10:55Z","cache_seconds":300,"methodology":"https://alion.io/methodology","terms":"https://alion.io/terms","contact":"https://alion.io/contact","api":"https://alion.io/developers","about":"Alion is a live layer of people, companies and AI agents: who they are, whether they are real and active right now, what they do and how to work with them, readable by people and by agents and paid per call.","catalog":"https://alion.io/catalog.json","usage":{"tier":"crawler_verified","counted_by":"address","units_charged":1,"used_today":26024,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-16T00:00:00Z"}},"offers":[{"id":"company.slices","title":"One company in depth, by slice","status":"live","price":{"credits":0.02,"usd":0.002,"plus_per_slice":{"credits":0.05,"usd":0.005}},"unit":"per company, plus each slice with data","note":"the employer in depth","call":{"mcp_tool":"get_company","arguments":{"id":2031237},"rest":"https://alion.io/mcp/rest/get_company?id=2031237"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Ffirebird-mlops-engineer-ml-platform-and-delivery"},{"id":"market.stats","title":"A market slice: pay, demand and time to fill","status":"live","price":{"credits":1,"usd":0.1},"unit":"per slice","note":"pay, demand and time to fill for this role and place","call":{"mcp_tool":"market_stats"},"human":"https://alion.io/catalog?offer=market.stats&for=job%2Ffirebird-mlops-engineer-ml-platform-and-delivery"},{"id":"job.search","title":"Open jobs by role, technology, place, pay and visa","status":"live","price":{"credits":0.02,"usd":0.002},"unit":"per posting in a list","note":"similar open postings","call":{"mcp_tool":"search_jobs"},"human":"https://alion.io/catalog?offer=job.search&for=job%2Ffirebird-mlops-engineer-ml-platform-and-delivery"},{"id":"company.verify","title":"Is this company real and active right now","status":"pilot","price":null,"unit":"per company","request":{"url":"https://alion.io/catalog/request","method":"POST","body":"{\"offer\": \"company.verify\", \"for\": \"job/firebird-mlops-engineer-ml-platform-and-delivery\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Ffirebird-mlops-engineer-ml-platform-and-delivery"}]}