Senior AI Solutions Engineer

Key Responsibilities :

1.Customer Onboarding & Platform Configuration 

• Provision multi-tenant environments: tenant creation, log file type registration, product family configuration, severity thresholds, and API key management. 

• Guide customers through LogIQ's Signature Onboarding Wizard. 

• Configure per-tenant defaults and document every configuration decision in customer-specific runbooks for long-term maintainability. 

• Validate the full detection lifecycle end-to-end on customer log samples before any go-live, including quality benchmarks on hold-out data. 

2. Streaming Log Ingestion & Proactive Monitoring 

• Set up real-time log stream ingestion pipelines — Kafka, Kinesis, Fluentd, syslog-ng, or customer-native agents — into LogIQ's streaming layer. 

• Configure the Anomaly Detection engine: define healthy baselines, tune sensitivity thresholds, and map deviation patterns to specific signature triggers. 

• Wire streaming triggers to the RCA Agent so that when an anomaly fires, root-cause investigation begins automatically with no human intervention. 

• Monitor stream health: lag, throughput, parsing error rates, and alert on pipeline degradation before it affects customer outcomes. 

• Work with customers to identify which log sources to prioritize for streaming vs. batch ingestion, balancing latency requirements against infrastructure cost. 

3. RCA Agent Configuration & Knowledge Enrichment 

• Ingest and index customer knowledge articles, historical case resolutions, and equipment documentation into the RCA Agent's retrieval layer (OpenSearch + pgvector). 

• Configure evidence-weighting rules so the RCA Agent knows which sources to trust most for a given equipment type or failure mode. 

• Tune reasoning prompts and retrieval strategies based on observed RCA quality — iterating until root-cause accuracy meets the customer's acceptance criteria. 

• Build fix-strategy libraries: map known root causes to recommended remediation steps, pulling from customer SOPs and historical tickets. 

• Validate RCA output against historical cases where the true root cause is known; track precision and recall over iteration cycles. 

4. Custom Demo Engineering 

• Ingest, clean, and pre-label customer-provided log samples to build compelling, domain-specific demos that speak directly to the customer's operational pain. 

• Demonstrate both reactive (case upload → signature detection → RCA → fix recommendation) and proactive (live stream → anomaly trigger → automated RCA) workflows against real data. 

• Create demo scripts, scenario walkthroughs, before/after MTTR comparisons, and leave-behind documentation for prospects. 

• Adapt demos quickly to new industries or log types — a customer in manufacturing should see their alarm formats, their fault patterns, their fix vocabulary. 

5.  Agent Tool & Skill Development 

• Design, build, and register new LangGraph agent tools as customer use cases demand — e.g., a tool that queries a customer's CMDB, pulls ticket history from ServiceNow, or fetches firmware changelogs from an internal API. 

• Package reusable capabilities as LogIQ Skills: self-contained, versioned bundles of tools, prompts, and configuration that can be applied across customers in the same domain. 

• Maintain a tool allowlist and review process so new tools integrate safely with the agent's execution context and tenant isolation guarantees. 

• Contribute high-quality tools back to the platform's shared tool library so the whole team benefits. 

6.  Log Parser & Data Connector Development 

• Write custom log parsers fo