{"id":37786,"url":"https://alion.io/job/pragmatike-ml-ops-engineer-emea-remote","title":"ML Ops Engineer (EMEA Remote)","company":{"id":8591,"name":"Pragmatike","domain":"pragmatike.com","url":"https://alion.io/company/pragmatike","size_band":"51-200","is_staffing_agency":true,"employer_type":"agency","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"B","score":75,"open_postings":5,"ghost_share":0,"stale_share":1,"repost_share":0,"time_to_fill_p50_days":41,"computed_at":"2026-10-01T05:45:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"middle","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_regions","remote_scope_basis":"posting_text","remote_working_hours":{"label":"EMEA time zones","utc_offset_min":0,"utc_offset_max":3},"hiring_geo_confidence":"inferred","locations":[],"countries":[],"hiring_countries":["HR","CZ","IT","PL","RO","RS","ES","TR","UA","AL","EE","GR","LV","LT","MT","PT","AM","DE","FR","AE","GB","AT","BE","BA","BG","CY","DK","EG","FI","HU","IE","IL","KE","NL","NG","NO","SA","ZA","SE","CH"],"hiring_countries_total":116,"salary":null,"salary_estimate":{"min_usd":53000,"max_usd":152000,"period":"year","method":"global_role_cell_scaled_by_country","sample_n":585},"experience_years_min":4,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"CI/CD","optional":false},{"name":"Machine Learning","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Terraform","optional":false},{"name":"TGI","optional":false},{"name":"Triton","optional":false},{"name":"vLLM","optional":false},{"name":"CUDA","optional":true},{"name":"CUDA Toolkit","optional":true},{"name":"GDPR","optional":true},{"name":"Kubeflow","optional":true},{"name":"MLFlow","optional":true},{"name":"ROCm","optional":true}],"status":"live","first_seen_at":"2026-07-10T18:51:25Z","employer_posted_date":"2026-09-23","last_verified_at":"2026-10-01T08:38:02Z","board_verified":true,"closed_at":null,"days_open":82,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":82},"description":"Location: Fully remote (EMEA timezone)\nStart date: ASAP\nLanguages: Fluent English required\nIndustry: Cloud Computing / AI / European Deep-Tech SaaS\nAbout the Role\nPragmatike is recruiting on behalf of a fast-scaling, well-funded distributed cloud infrastructure startup building next-generation AI-native cloud services. The company is redefining how compute is delivered by providing GPU-powered infrastructure for AI/ML workloads, secure storage, and high-speed data transfer through a decentralized architecture that significantly reduces environmental impact compared to traditional cloud providers.\nWe are seeking a ML Ops Engineer with strong experience in production-grade model serving and infrastructure for AI systems. This is a highly technical, hands-on role focused on building scalable, reliable, and efficient ML inference platforms powering real-time AI applications.\nYou will be responsible for designing and operating the core infrastructure that serves machine learning models at scale. You will work closely with infrastructure, platform, and applied AI teams to ensure high availability, low latency, and cost-efficient inference systems. Strong ownership, production mindset, and experience with distributed GPU systems are essential.\nYour Responsibilities\nBuild and operate production-grade model serving infrastructure using frameworks such as vLLM, TGI, Triton, or equivalent\n\nDesign and implement robust deployment pipelines with blue/green and canary rollout strategies for ML models\n\nDevelop and maintain auto-scaling systems, multi-model serving architectures, and intelligent request routing layers\n\nOptimize GPU utilization, memory efficiency, network throughput, and model artifact storage performance\n\nDesign observability systems for tracking inference latency, throughput, GPU usage, cost metrics, and system health\n\nManage model registries and CI/CD pipelines enabling automated and reproducible model deployments\n\nOwn the full lifecycle of ML systems from development through production, including operational support and on-call responsibilities\n\nDefine engineering best practices and contribute to platform scalability in a fast-moving startup environment\n\nRequired Qualifications\n4+ years of experience in ML Ops, Platform Engineering, SRE, or similar infrastructure roles focused on ML systems\n\nHands-on experience with model serving frameworks such as vLLM, TGI, Triton, or equivalent\n\nStrong background in container orchestration and operating GPU-based workloads in production\n\nExperience with MLOps tooling including model registries, experiment tracking, and automated deployment pipelines\n\nProficiency in Python and infrastructure-as-code tools (e.g., Terraform, Helm, or similar)\n\nStrong understanding of distributed systems, performance tuning, and production reliability engineering\n\nAbility to effectively use AI coding assistants to accelerate development and debugging workflows\n\nOwnership mindset with the ability to operate independently in a remote-first environment\n\nPreferred Qualifications\nExperience with ML platforms such as Kubeflow, MLflow, or KubeAI\n\nKnowledge of GPU scheduling, CUDA/ROCm optimization, or multi-tenant inference systems\n\nExperience with cost optimization across different GPU types and inference workloads\n\nBackground in early-stage startups or greenfield infrastructure projects\n\nProven experience building production systems from scratch rather than maintaining legacy platforms\n\nWhy Join Us\nTake ownership of critical infrastructure powering a rapidly scaling AI-native cloud platform\n\nBuild foundational ML inference systems from the ground up in a high-growth, well-funded startup\n\nWork at the intersection of distributed systems, GPU computing, and sustainable cloud architecture\n\nGain deep expertise in next-generation AI infrastructure and large-scale model serving systems\n\nInfluence core engineering decisions and define best practices that will scale with the company.\n\nPragmatike is committed to a fair, transparent, and inclusive recruitment process. 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