{"id":3355578,"url":"https://alion.io/job/gsk-senior-solution-support-engineer","title":"Senior Solution Support Engineer","company":{"id":6649,"name":"GSK","domain":"gsk.com","url":"https://alion.io/company/gsk","size_band":"1001-5000","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Workday","truth_index":{"grade":"A","score":86,"open_postings":56,"ghost_share":0,"stale_share":0.768,"repost_share":0.018,"time_to_fill_p50_days":17,"computed_at":"2026-10-29T05:49:05Z"}},"role":"Support","role_family":"Support","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":["Bengaluru, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":21000,"max_usd":50000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":8},"experience_years_min":15,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Agentic Workflows","optional":false},{"name":"AI Agents","optional":false},{"name":"AIOps","optional":false},{"name":"CI/CD","optional":false},{"name":"Hallucination","optional":false},{"name":"LLM","optional":false},{"name":"LLM Guardrails","optional":false},{"name":"LLMOps","optional":false},{"name":"Platform Engineering","optional":false},{"name":"Python","optional":false},{"name":"RAG","optional":false},{"name":"SLI/SLO/SLA","optional":false},{"name":"Veeva","optional":false},{"name":"Azure","optional":true},{"name":"FinOps","optional":true},{"name":"GCP","optional":true},{"name":"ITIL","optional":true},{"name":"Machine Learning","optional":true}],"status":"live","first_seen_at":"2026-10-27T19:49:39Z","employer_posted_date":"2026-10-27","last_verified_at":"2026-10-29T21:43:46Z","board_verified":true,"closed_at":null,"days_open":2,"trust":{"level":"ok","repost_count":null,"flags":[],"days_open":2},"description":"About the Role:\nGSK’s Enterprise AI team is expanding its ability to architect, build, and run complex AI solutions across the enterprise. Senior Solutions Engineers sit at the intersection of technical architecture, hands-on AI engineering, and production operations - ensuring that AI capabilities translate reliably into production value and keep delivering it after go-live.\nAs a Senior Solutions Engineer you will own the technical solution design for complex AI integrations and personally build and operate them, working across EAI engineering, business technology teams, external partners, and strategic suppliers to ensure that what gets built can be deployed, integrated, maintained, and operated at enterprise scale. This is a hands-on role: you will design, build, and review AI systems yourself - including LLM, agentic, and retrieval-based solutions - working within the architectural standards, engineering practices, and lifecycle framework set by the Staff Solutions Engineer, and contributing to how they evolve. You bring both the architectural depth to make the right design choices and the communication skills to earn confidence across technical and business audiences.\nThe role carries direct accountability for the AI products and integrations you deliver. Expect to spend roughly 40% of your time on production operations and support - keeping the lights on (KTLO), meeting SLAs and service metrics, and day-to-day coordination with strategic suppliers - and roughly 60% on engineering, with a deliberate focus on using that engineering to automate operations and steadily reduce the operational load.\nYou will use AI agents as part of your engineering and operations toolkit, supervising their output, to keep lowering the cost at which value is delivered for the solutions you own. As those solutions scale in deployment, you are responsible for identifying when the current engineered solution needs to be refactored and/or optimised, and for recommending and delivering that change - escalating portfolio-level cases to the Staff Solutions Engineer.\nWhy This Role Exists:\nAs the Enterprise AI portfolio matures, the complexity of integration, deployment, and operations grows. Many of the most valuable AI opportunities require deep technical solutioning before engineering can begin, hands-on engineering to build them, and disciplined operation once they are live. In this role you will be the person who:\nCloses the gap between AI capability and enterprise deployment readiness through rigorous technical solutioning.\nEnables faster delivery by resolving integration complexity early, before it becomes a production blocker.\nBuilds and ships production AI solutions hands-on, so that architectural decisions are grounded in working code rather than slideware.\nKeeps products healthy in production by owning KTLO, SLAs, and service metrics for the solutions you deliver.\nAutomates the run through monitoring, AIOps, and AI agents, so reliability improves and the cost to operate falls as the portfolio grows.\n Governs the lifecycle by applying Product Lifecycle Management (PLM) discipline - stage gates, versioning, change control, and traceability - to every AI product you deliver.\nRaises the bar on solution quality by applying and contributing to architectural standards across complex engagements.\nBuilds trust with business technology partners and enterprise architects through credible, well-grounded technical guidance.\nKey Responsibilities:\nSolution Architecture & Technical Design\nOwn technical solution design for complex AI integrations - from requirements through to detailed architecture, component selection, and integration specification.\nDefine integration patterns for connecting AI capabilities with enterprise systems: SAP, Veeva, Salesforce, clinical data platforms, and GSK’s data infrastructure.\nDesign for operability so that observability, SLOs, runbooks, and support requirements are built into the solution from the start rather than added after go-live.\nProduce solution architecture documentation that enables downstream engineering teams to build with confidence and minimises ambiguity at handoff.\nHands-On AI Engineering (approx. 60% of role)\n Build and ship production AI solutions - including LLM-based applications, agentic workflows, retrieval-augmented generation (RAG), and ML model serving - integrated with GSK enterprise systems.\nWrite production-quality code and reference implementations, with tests, evaluation harnesses, guardrails, and CI/CD for the AI systems you deliver.\nApply the function’s AI engineering standards - prompt and model versioning, evaluation, secure deployment - and contribute improvements back to them.\nEvaluate models, frameworks, and tooling through hands-on benchmarking of quality, latency, cost, and risk, and recommend the right choice for each engagement.\nPartner with Forward Deployed Engineers and platform engineers to take proven prototypes through to hardened, supportable production services, and own the handoff for your engagements.\nUse AI agents and coding assistants as part of daily engineering and operations, supervising and quality-assuring their output to lower the cost of delivering the solutions you own.\nDeployment Readiness & Technical Assurance:\nLead technical due diligence for complex deployments - identifying integration risks, data dependencies, security considerations, and operational requirements before build begins.\nDrive deployment readiness assessments ensuring AI solutions meet enterprise architecture standards, security policy, and regulatory requirements prior to production.\nProvide technical assurance throughout delivery - reviewing design decisions, flagging architectural drift, and escalating risks to engineering and product leadership.\nProduct Lifecycle Management (PLM) for Enterprise AI:\nApply the PLM framework and stage gates to every AI product and integration you deliver, preparing the release evidence - evaluation results, security, data privacy, regulatory and quality requirements.\nMaintain accurate lifecycle records for your products - models, versions, prompt versions, dependencies, owners, and status - and drive change management, deprecation, and retirement.\n Ensure traceability and auditability across the lifecycle (data lineage, model and prompt versions, approvals, and decisions), suitable for a regulated pharma environment.\nFeed delivery and operational experience back into the PLM framework so that gates and criteria improve over time.\nProduction Operations & Support (approx. 40% of role):\nOwn production operation and support for the AI integrations and products you deliver, including KTLO activities such as patching, upgrades, dependency and certificate management, and break-fix.\n Deliver against SLAs, SLOs, and service metrics (availability, latency, incident response and resolution times, change success rate, cost to serve) for your services, reporting performance to the Staff Solutions Engineer and business owners.\nDiagnose and resolve integration failures and lead incident response for your services, running post-incident reviews that drive root-cause fixes rather than repeated manual remediation.\nWork day to day with strategic suppliers and platform vendors on delivery - raising and tracking tickets, coordinating support escalations, and flagging performance or SLA issues to the Staff Solutions Engineer.\nMaintain runbooks and knowledge base articles for your services, and participate in the on-call and escalation model.\nTrack operational toil for the services you own and convert it into automation.\nIntelligent Operations & Automation:\n Build and operate observability for your services - monitoring of model quality, drift, hallucination and safety signals, latency, availability, and cost - with defined SLOs, alerting, and runbooks (AIOps / LLMOps / MLOps).\nApply AI and agents to operations itself - automated triage, root-cause analysis, runbook execution, cost and capacity optimisation, and self-service support for product teams.\nFeed production usage and operational data back into design, evaluation, and PLM decisions so the portfolio improves with every release.\nContribute to operating-model standards - ownership, support tiers, escalation, and the FDE-to-SE handoff - drawing on what you learn running real services.\nSolution Evolution & Cost-to-Value:\n Track unit-economics and value metrics for the solutions you own (for example cost per transaction or outcome, and run cost against business value) and use them to prioritise optimisation work.\nAs your solutions scale in deployment, identify when the current engineered solution needs to be refactored and/or optimised - for performance, reliability, cost, or maintainability - and make the case, plan the work, and deliver the change; escalate portfolio-level cases to the Staff Solutions Engineer.\n Monitor scale signals (usage growth, latency, cost per unit, incident trends, architectural limits) so that refactor and optimisation decisions are triggered early, and feed the outcomes into reusable patterns.\nOwn the technical roadmap for integration capabilities, identifying where investment in common patterns will reduce cost and risk across the portfolio.\nContribute to platform engineering decisions based on deployment and integration experience - feeding real-world constraints back into how the platform evolves.\nPartner Engagement & Technical Governance:\nRepresent EAI’s technical architecture in conversations with enterprise architecture, data governance, security, and vendor partner teams.\nBuild and maintain relationships with business technology leads across GSK to support alignment and accelerate approval of complex deployments.\nContribute to EAI’s technical standards and architecture decision records, ensuring decisions are well-reasoned, documented, and reusable.\nRequired Experience:\n15+ years in enterprise solution architecture, software or AI engineering, or technical pre-sales/post-sales engineering, with a track record of designing complex integrations.\nRecent, hands-on AI engineering experience: you have personally built and deployed production AI/ML or LLM-based systems (for example RAG, agentic workflows, model serving, evaluation pipelines) and can write, review, and debug code in languages such as Python.\nStrong working knowledge of enterprise integration patterns, APIs, event-driven architectures, and data exchange standards.\nPractical experience with MLOps/LLMOps and cloud-native deployment (CI/CD, containers, infrastructure as code, observability) sufficient to make sound decisions about AI deployment, integration, and operability.\nExperience supporting production operations for enterprise platforms or AI services, including KTLO, SLA/SLO and service-metric delivery, incident and problem management, and on-call or escalation models.\nExperience using automation - and ideally AI agents - to reduce operational toil and the cost of delivery or operation.\nWorking knowledge of Product Lifecycle Management (PLM) or equivalent lifecycle governance for technology, data, or AI products, including stage gates, versioning, change control, and retirement.\nExperience working with vendors or suppliers on delivery, including SLA follow-up, support escalations, and performance conversations.\nTrack record of recognising when a system needs to be refactored or optimised as it scales, and delivering that work (performance tuning, re-architecture, cost optimisation).\nExperience operating in a regulated enterprise environment (pharma, healthcare, or financial services preferred).\nConfidence engaging with senior technical and business stakeholders: running workshops, producing crisp architecture artefacts, and defending design choices.\nPreferred Experience:\nExperience in an AI-native company or equivalent AI-first engineering environment.\nFamiliarity with ITIL or SRE practices (error budgets, blameless post-incident reviews, change management) applied to AI and dat...","description_format":"text","description_chars":15825,"description_truncated":true,"requirements":{"experience_years_min":15,"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":["Prescription Drugs","Vaccines","Oncology Therapeutics","Infectious Disease Therapeutics"],"lifecycle":[{"event":"open","at":"2026-10-27T19:49:39Z"}],"visa":[],"liveness":{"score":63,"band":"ok","label":"Likely open","p_open":1,"p_active":0.632,"p_room":1,"age_days":1,"expected_fill_days":17,"reasons":["conf:2","stale_co","velocity","win:early","comp:brand"],"computed_at":"2026-10-29T05:49:05Z"},"pay":null,"html_url":"https://alion.io/job/gsk-senior-solution-support-engineer","json_url":"https://alion.io/job/gsk-senior-solution-support-engineer.json","meta":{"generated_at":"2026-10-29T22:58:12Z","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":33899,"day_limit":null,"remaining_today":null,"minute_limit":300,"resets_at":"2026-10-30T00:00:00Z"}},"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":6649},"rest":"https://alion.io/mcp/rest/get_company?id=6649"},"human":"https://alion.io/catalog?offer=company.slices&for=job%2Fgsk-senior-solution-support-engineer"},{"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%2Fgsk-senior-solution-support-engineer"},{"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%2Fgsk-senior-solution-support-engineer"},{"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/gsk-senior-solution-support-engineer\", \"note\": \"what you need it for\"}"},"human":"https://alion.io/catalog?offer=company.verify&for=job%2Fgsk-senior-solution-support-engineer"}],"agent":{"mcp":"https://alion.io/mcp/jobs","mcp_open":"https://alion.io/mcp/open","next":[{"tool":"get_job","arguments":{"id":3355578}},{"tool":"search_jobs","arguments":{"company":"gsk.com","detail":"compact"}},{"tool":"match_people","arguments":{"job_id":3355578},"note":"people open to offers who fit this vacancy"}],"note":"The same data through MCP tools: https://alion.io/mcp/open needs no sign-in (compact searches, postings, people); https://alion.io/mcp with an Alion account has everything. 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