About Us
Neuron7.ai is a rapidly growing AI‑first SaaS company focused on building a category‑defining Service Resolution Intelligence platform. Backed by leading venture capital firms in Silicon Valley and a distinguished group of angel advisors and investors, Neuron7 is widely recognized as a startup to watch. Our platform enables enterprises to resolve complex operational issues faster by delivering accurate root‑cause analysis and fix recommendations in seconds—leveraging a combination of structured data, unstructured data, and advanced AI agents. Learn more at Neuron7.ai.
Why Join Us
- Work at the frontier of applied AI - LangGraph, LLM, streaming anomaly detection, evidence-based RCA reasoning on real enterprise problems, not toy datasets.
- Both modes of LogIQ (reactive and proactive) are expanding fast; you'll help define how the platform scales to new industries and log ecosystems.
- Your work ships quickly and visibly: demos you build turn into signed contracts; parsers you write run in production within days; tools you create become platform features.
- Engineering depth with customer exposure — you commit to the main repo and influence product direction, while building relationships with some of the world's most complex operations teams.
- Bangalore team with global reach — you'll work closely with the US product and engineering leadership, giving you visibility and mentorship far beyond a typical India engineering role.
The Role
We are hiring AI Solutions Engineers based in Bangalore who will own the full customer journey from first onboarding call to a live, production LogIQ deployment reactive and proactive. You will work directly with enterprise customers, understand their operational domain, prepare their data, configure the platform, and build compelling demos that show exactly how LogIQ reduces mean-time-to-resolution (MTTR) on their hardest problems.
This is not a support or ticket-handling role. You will write Python, build and register new agent tools, create custom log parsers, configure streaming pipelines, tune LLM prompts, debug async agent failures, and contribute directly to the core platform codebase. You are part engineer, part domain expert, and fully accountable for customer outcomes.
What You’ll Do
We are looking for an AI Solutions Engineer with 2–5 years of relevant experience to own the technical customer journey from onboarding and configuration to live production deployments and advanced AI customization. In this role, you will blend strong software engineering skills, hands‑on AI/LLM experience, and customer‑facing problem solving. You will help enterprises operationalize LogIQ by preparing their data, configuring AI agents, building compelling demos, tuning models, and ensuring successful outcomes at scale.
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 for proprietary or undocumented equipment formats (Python, plugged into the FastAPI parser registry).
- Build data connectors for customer-specific ingestion sources: REST APIs, SFTP drops, database exports, or cloud storage buckets.
- Define record-splitting rules, type classifiers, and deep-parsed field schemas for new log file types using the Signature Onboarding pipeline.
- Maintain a parser test suite — real sample lines, expected field outputs — so parsers don't regress across platform updates.
7. Platform Customization & Code Contributions
- Tune LLM system prompts, memory strategies, context windows, and few-shot examples based on observed agent behavior on customer data.
- Modify the signature workflow DAG to handle customer-specific detection logic that the automated agent generation doesn't cover out of the box.
- Ship targeted bug fixes and feature additions back to the core platform codebase.