{"id":728897,"url":"https://alion.io/job/nace-senior-mlops-engineer","title":"Senior MLOps Engineer","company":{"id":678797,"name":"Nace.AI","domain":"nace.ai","url":"https://alion.io/company/nace-3","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Ashby","truth_index":{"grade":"D","score":51,"open_postings":17,"ghost_share":0.824,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":null,"computed_at":"2026-11-01T06:10:25Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Palo Alto, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":164000,"max_usd":315000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":1332},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AWQ","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Docker","optional":false},{"name":"GCP","optional":false},{"name":"GGUF","optional":false},{"name":"GPTQ","optional":false},{"name":"Knowledge Distillation","optional":false},{"name":"Kubeflow","optional":false},{"name":"Kubernetes","optional":false},{"name":"KV Cache","optional":false},{"name":"LLM","optional":false},{"name":"MLFlow","optional":false},{"name":"Model Distillation","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"Quantization","optional":false},{"name":"Ray","optional":false},{"name":"SGLang","optional":false},{"name":"Terraform","optional":false},{"name":"TGI","optional":false},{"name":"vLLM","optional":false},{"name":"Weights & Biases","optional":false},{"name":"DPO","optional":true},{"name":"Fine-tuning","optional":true},{"name":"RLHF","optional":true},{"name":"Spark","optional":true}],"status":"live","first_seen_at":"2026-07-16T22:22:02Z","employer_posted_date":"2026-07-16","last_verified_at":"2026-11-02T04:32:26Z","board_verified":true,"closed_at":null,"days_open":108,"trust":{"level":"ghost","repost_count":0,"flags":["stale","company_stale"],"days_open":107},"description":"Palo Alto, CA | Full-Time | On-site\nAbout Us:\nAt Nace AI, we are redefining how professional services operate by delivering Sovereign Specialized Intelligence. As an applied research and product company, we equip enterprises with a comprehensive AI stack to build customized, secure intelligence tailored to their unique business needs.\nDriven by advanced Small Language Models and our dynamic metamodel framework, our flagship platforms - Nace Data Intelligence and the Nace SLM Cloud - enable true end-to-end business process automation. The result is transformative ROI: professional services firms using Nace AI are currently recovering 1,000 hours per client engagement, drastically reducing overhead and accelerating delivery.\nThe work we are doing has a meaningful impact across industries, and every hire at Nace AI plays a critical role in shaping the company’s trajectory. This is a unique opportunity to join a high conviction AI company at an early stage and directly influence its growth.\nIf building a world-class AI team from the ground up excites you, we’d love to talk.\nRole Overview:\nAs a Senior MLOps Engineer, you will own the infrastructure that takes Nace.AI 's models from research to reliable, production-grade systems. Our infrastructure generates task-specific Small Language Models (SLMs) in real time - which means our training, serving, and evaluation infrastructure isn't an afterthought; it is the product. You will design and operate the pipelines, orchestration, and serving layers that allow us to train, deploy, monitor, and continuously improve many specialized models at once, with the reliability that high-stakes audit, compliance, and finance workflows demand. This role sits at the intersection of ML engineering, LLM inference infrastructure, and platform reliability, and requires both strong systems instincts and hands-on execution.\nKey Responsibilities:\nDesign, build, and operate end-to-end ML infrastructure: training orchestration, experiment tracking, model registries, CI/CD for models, and automated evaluation pipelines.\n\nOwn LLM/SLM serving infrastructure - scale low-latency, high-throughput inference using frameworks like vLLM, including batching, caching, and autoscaling strategies.\n\nBuild and manage multi-GPU training and inference clusters (scheduling, utilization, cost optimization) across cloud and on-prem environments.\n\nImplement observability for models in production: latency, throughput, drift, regression, and quality monitoring with actionable alerting.\n\nApply inference-time optimizations - quantization (AWQ, GPTQ, FP8/GGUF), distillation support, KV-cache management, and deployment tuning - in partnership with our ML and Research Engineers.\n\nHarden our stack for enterprise deployment: reproducibility, versioning, access controls, and audit-ready traceability of model behavior.\n\nSet MLOps best practices and tooling standards as an early, senior member of the infrastructure team.\n\nQualifications:\n5+ years of experience in MLOps, ML infrastructure, or platform engineering, with substantial production ownership.\n\nProven experience deploying and scaling LLM, inference infrastructure in production, including model serving frameworks such as TRT, vLLM, SGLang or TGI.\n\nStrong proficiency with Kubernetes, containerization (Docker), and infrastructure-as-code (Terraform or similar).\n\nHands-on experience with GPU cluster management and distributed training/serving environments.\n\nProficient in Python with a strong track record of building substantial, maintainable systems.\n\nExperience with ML pipeline and orchestration tooling (e.g., Airflow, Kubeflow, Ray, MLflow, Weights & Biases).\n\nSolid foundation in computer science fundamentals and cloud architecture (AWS, GCP, or Azure).\n\nBS degree in CS or related technical field.\n\nSelf-starter comfortable working in a fast-paced, dynamic environment.\n\nPreferred Qualifications:\nMS in CS or related technical field.\n\nExperience operating multi-node GPU training infrastructure.\n\nHands-on experience with quantization techniques (AWQ, GPTQ, FP8/GGUF) and other inference-time optimizations.\n\nFamiliarity with data processing stacks such as Spark and Airflow.\n\nExperience supporting fine-tuning workflows for LLMs/VLMs (instruction tuning, RLHF/DPO pipelines).\n\nExperience in regulated or enterprise environments where reliability, security, and auditability are first-class requirements.\n\nContributor to open-source ML infrastructure projects.\n\nWhy Nace AI?\nPedigree: Work with a team from top-tier institutions and companies, backed by the best VCs in the world.\n\nImpact: You are joining early enough to 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