{"id":169438,"url":"https://alion.io/job/lam-research-senior-ai-observability-engineer","title":"Senior AI Observability engineer","company":{"id":58649,"name":"Lam Research","domain":"lamresearch.com","url":"https://alion.io/company/lam-research","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Eightfold","truth_index":{"grade":"C","score":57,"open_postings":692,"ghost_share":0.438,"stale_share":0.046,"repost_share":0.731,"time_to_fill_p50_days":71,"computed_at":"2026-10-17T05:46:00Z"}},"role":"AI/ML","role_family":"AI/ML","seniority":"senior","employment_type":null,"work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Fremont, United States"],"countries":["US"],"hiring_countries":[],"hiring_countries_total":0,"salary":{"min":92000,"max":211000,"currency":"USD","period":"year","gross":null,"usd_annual":211000},"salary_estimate":null,"experience_years_min":null,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"A/B Testing","optional":false},{"name":"AI Agents","optional":false},{"name":"AIOps","optional":false},{"name":"Anomaly Detection","optional":false},{"name":"AutoGen","optional":false},{"name":"AWS","optional":false},{"name":"Azure","optional":false},{"name":"BGP","optional":false},{"name":"CI/CD","optional":false},{"name":"DNS","optional":false},{"name":"Embeddings","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Hallucination","optional":false},{"name":"HPC","optional":false},{"name":"Human-in-the-Loop","optional":false},{"name":"ITSM","optional":false},{"name":"LangGraph","optional":false},{"name":"Least Privilege","optional":false},{"name":"LLM","optional":false},{"name":"LLM Evaluation","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"MPLS","optional":false},{"name":"OpenTelemetry","optional":false},{"name":"OSPF","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Prompt Engineering","optional":false},{"name":"RAG","optional":false},{"name":"Semantic Kernel","optional":false},{"name":"ServiceNow","optional":false},{"name":"TCP/IP","optional":false},{"name":"Time Series Forecasting","optional":false},{"name":"Tool Use","optional":false},{"name":"LangChain","optional":true}],"status":"live","first_seen_at":"2026-01-15T23:10:29Z","employer_posted_date":"2026-08-27","last_verified_at":"2026-10-18T07:12:20Z","board_verified":true,"closed_at":null,"days_open":275,"trust":{"level":"ghost","repost_count":6,"flags":["stale","repost"],"days_open":275},"description":"The group you’ll be a part of\nWe are seeking a hands-on Senior AIOps Reliability Engineer to build the AI-native operations layer for our hybrid enterprise estate. You will design and ship LLM-based agents, retrieval-augmented knowledge pipelines, and machine-learning anomaly detection that find, triage, and remediate incidents across public cloud, private cloud, and on-premises data centers, all resting on strong SRE and network engineering fundamentals.\nOur environment spans Azure, AWS, and Google Cloud alongside on-premises data centers, colocation sites, and manufacturing and HPC facilities, so cloud-agnostic design and hybrid network fluency matter more than depth in any single provider. This is a deep individual-contributor role: you will write the code, instrument the telemetry, tune the models, and own the reliability of both the infrastructure and the AI systems operating on it.\nThe impact you’ll make\nJoin Lam as an IT Engineer, where you'll be at the forefront of designing, analyzing, and implementing applications and systems that form the foundation of our infrastructure. As a crucial member of our IT team, you'll contribute your technical assistance and guidance to projects for various systems and infrastructures. Acting as a technical liaison, you'll address complex business problems with automated systems solutions. Your expertise will be instrumental in driving Lam's commitment to innovation and efficiency.\nWhat you’ll do\nAI and Agentic Operations\nBuild agentic AI workflows using LLM agents, tool and function calling, and orchestration frameworks such as LangGraph, Semantic Kernel, AutoGen, or the Model Context Protocol, applied to autonomous fault detection, triage, and remediation. \nDevelop the AIOps intelligence layer: time-series anomaly detection, dynamic baselining, alert deduplication and correlation, event clustering, and predictive failure and capacity forecasting across infrastructure, network, and application telemetry from both cloud and on-premises sources. \nEngineer the retrieval knowledge fabric by chunking, embedding, and indexing runbooks, post-mortems, architecture documents, CMDB and ServiceNow records into a vector store, then tuning retrieval quality against measurable evaluations. \nShip AI-assisted incident response: automated summarization, root-cause hypothesis generation, blast-radius analysis, and telemetry-grounded draft post-mortems wired into the paging and ITSM toolchain. \nAutomate remediation safely through event-driven pipelines and configuration-management runbooks invoked by agents, with human-in-the-loop approval gates, scoped least-privilege boundaries, rollback paths, and complete audit trails for every autonomous action. \nOwn AI safety and governance in production: guardrails, prompt-injection defense, hallucination and drift monitoring, PII redaction, and evaluation harnesses that gate every model or prompt change. \nRun LLMOps and MLOps, covering prompt and model versioning, offline and online evaluation, shadow and A/B testing, inference logging, token cost and latency observability, and CI/CD for every AI component. \nInstrument AI systems as first-class services with OpenTelemetry GenAI tracing, model SLOs, and quality, cost, and latency dashboards for every agent in production. \nWho we’re looking for\nBS, MS, or PhD in Computer Science, Engineering, or a related field, or equivalent practical experience. \nEight or more years in SRE, DevOps, infrastructure, observability, network engineering, or platform engineering, with a track record of shipping production systems yourself. \nProduction experience with LLM applications: prompt engineering, retrieval-augmented generation, embeddings and vector databases, function and tool calling, and agent orchestration. \nPractical use of machine learning for anomaly detection, forecasting, event correlation, and alert noise reduction on real operational telemetry. \nWorking knowledge of at least one major AI platform and the ability to remain portable across them, including private-network deployment, quota, and cost management. \nHands-on production experience with at least one major public cloud and the ability to design portable, cloud-agnostic patterns across the others, covering identity and access boundaries, compute, storage, managed databases, serverless functions, and event services. \nSolid on-premises infrastructure background across virtualization, storage, data-center operations, and private cloud platforms. \nStrong networking fundamentals spanning both worlds: TCP/IP, BGP and OSPF, VLANs and overlays such as VXLAN and EVPN, MPLS and SD-WAN, firewalls, load balancers, DNS, and cloud virtual network, VPC, and transit routing constructs. You can read a flow log or a routing table and reason about a failure. \nPreferred qualifications\nOur commitment\nWe believe it is important for every person to feel valued, included, and empowered to achieve their full potential. By bringing unique individuals and viewpoints together, we achieve extraordinary results.\nLam Research (\"Lam\" or the \"Company\") is an equal opportunity employer. Lam is committed to and reaffirms support of equal opportunity in employment and non-discrimination in employment policies, practices and procedures on the basis of race, religious creed, color, national origin, ancestry, physical disability, mental disability, medical condition, genetic information, marital status, sex (including pregnancy, childbirth and related medical conditions), gender, gender identity, gender expression, age, sexual orientation, or military and veteran status or any other category protected by applicable federal, state, or local laws. It is the Company's intention to comply with all applicable laws and regulations. Company policy prohibits unlawful discrimination against applicants or employees.\nLam offers a variety of work location models based on the needs of each role. Our hybrid roles combine the benefits of on-site collaboration with colleagues and the flexibility to work remotely and fall into two categories - On-site Flex and Virtual Flex. ‘On-site Flex’ you’ll work 3+ days per week on-site at a Lam or customer/supplier location, with the opportunity to work remotely for the balance of the week. ‘Virtual Flex’ you’ll work 1-2 days per week on-site at a Lam or customer/supplier location, and remotely the rest of the time.\nSalary\nCA San Francisco Bay Area Salary Range for this position: $92,000.00 - $211,000.00.\nThe above salary range for this position is relevant to applicants that reside or work onsite in the California, San Francisco Bay Area only. Salary offers will depend on factors that include the location you work from, your level, education, training, specific skills, years of experience and comparison to other employees already in this role. Actual salary may vary from salary offered due to numerous factors including but not limited to unpaid time off, unpaid leave, company mandated shutdown, and other relevant factors.\nOur Perks and Benefits\nAt Lam, our people make amazing things possible. 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