{"id":3867287,"url":"https://alion.io/job/talentiser-ai-architect-aiops","title":"AI Architect - AIOps","company":{"id":3806728,"name":"Talentiser","domain":"talentiser.com","url":"https://alion.io/company/talentiser","size_band":"11-50","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"staff","employment_type":null,"work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Delhi, India","Gurgaon, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":12,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"AI Agents","optional":false},{"name":"AIOps","optional":false},{"name":"Function Calling","optional":false},{"name":"GCP","optional":false},{"name":"Gemini","optional":false},{"name":"Google ADK","optional":false},{"name":"Google BigQuery","optional":false},{"name":"Google Cloud Run","optional":false},{"name":"Machine Learning","optional":false},{"name":"Model Context Protocol","optional":false},{"name":"Python","optional":false},{"name":"ServiceNow","optional":false},{"name":"Vertex AI","optional":false}],"status":"live","first_seen_at":"2026-11-06T11:39:40Z","employer_posted_date":null,"last_verified_at":"2026-11-06T11:39:40Z","board_verified":false,"closed_at":null,"days_open":2,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":2},"description":"Job Description:\n\nThe candidate will have responsibilities across the following functions:\n\nAI Engineering, Agentic Capability, and Product Leadership:\n\n- Lead the design, build, and operationalization of agentic AI capabilities for event correlation, incident triage, service health assessment, knowledge retrieval, decision support, and bounded remediation.\n\n- Own the product strategy, roadmap, backlog, release approach, and value measures for the TOC agentic capability.\n\n- Design human + AI workflows with clear reasoning, planning, tool use, memory, identity, traceability, and auditability.\n\n- Apply strong engineering judgement on code-first, low-code, multi-agent, and integration patterns based on risk, scale, control, and operational value.\n\n- Ensure every AI capability is measurable, supportable, production-ready, and safe to operate in live service environments.\n\n- Drive event-driven operations by treating logs, alerts, events, traces, service state, and customer impact signals as first-class operational data.\n\n- Improve telemetry quality, signal ownership, service mapping, dependency data, and operational context so AI and humans can reason from reliable evidence.\n\n- Create decision-oriented views that show what is at risk, what is credible, what action is safe, and what customer or business impact is likely.\n\n- Use operational data to identify reliability trends, leading indicators, toil reduction opportunities, and commercial or business impact where relevant.\n\nTOC Strategy, Operating Model, and Service Design:\n\n- Define the TOC agentic strategy and operating model, covering authority, workflows, incident handling, roles, governance, and execution boundaries.\n\n- Establish the TOC as an event-driven control plane for live operational risk and customer-impacting technology events.\n\n- Create a phased roadmap from MVP to scale, proving value early and building confidence for wider adoption.\n\n- Clarify where TOC capability starts and stops, keeping product, platform, infrastructure, and support team accountability explicit.\n\n- Use AI, automation, and operational context to improve incident detection, triage, escalation, recovery, and root cause learning.\n\n- Reduce alert noise, duplicated effort, manual triage, and avoidable operational toil.\n\n- Ensure the capability works across hybrid environments, including legacy platforms, cloud services, SaaS, networks, booking, and clinical systems where they are in scope.\n\n- Build credibility by demonstrating measurable improvements in live service outcomes, not just prototypes.\n\n- Create strong collaboration with product and domain owners so telemetry, automation, and delegated authority are used without weakening team accountability.\n\n- Build the internal skills, standards, and support model, and run-operate-improve disciplines needed to sustain the capability.\n\n- Link roadmap priorities to measurable improvements in service reliability, operational effectiveness, and business impact.\n\nRequirements:\n\n- Experience in AI engineering, agentic systems, automation, and observability, with a track record of applying these capabilities to improve live service outcomes.\n\n- Strong understanding of observability, telemetry, event management, service mapping, operational data quality, and the use of signals to support decision-making.\n\n- Experience introducing AI, automation, or AIOps capability into operational environments in a way that is safe, measurable, supportable, and adopted by users.\n\n- Credibility in live-service environments, with enough technology operations knowledge to understand incidents, hand-offs, escalation, service continuity, and operational risk.\n\n- Strong stakeholder leadership and communication skills, with the ability to influence senior leaders, engineers, operators, control functions, and suppliers.\n\n- Experience building or leading multidisciplinary teams through transformation, ambiguity, and organizational change.\n\nParticularly Relevant Experience:\n\n- Experience with ServiceNow as the operational system of record, including incident and workflow integration.\n\n- Experience with Google Cloud or comparable cloud platforms used for agentic and data workloads, including familiarity with patterns such as Gemini Enterprise Agent Platform, Python-based ADK development, Cloud Run services, managed or proxied MCP access, Apigee, BigQuery, Vertex AI, AlloyDB, GCS, and secure API mediation.\n\n- Experience operating in regulated or high-control environments where resilience, auditability, customer impact, and control discipline are critical.\n\nSkills\nArtificial Intelligence, Machine Learning, Agentic AI, ServiceNow, Vertex AI, BigQuery, Python, Cloud, Generative AI, Observability Services","description_format":"text","description_chars":4726,"description_truncated":false,"requirements":{"experience_years_min":12,"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":["Recruiting & 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