Ai

• 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.

• 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.

• 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.

• 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.

• 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.

• 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 — you are a contributor, not just a consumer.

• Debug async pipeline failures.

• Own the technical relationship for your customer portfolio: onboarding calls, weekly syncs, async Slack/email, and escalation handling.

• Translate customer domain knowledge — telecom alarm semantics, SCADA event codes, IT operations terminology — into LogIQ configuration and agent guidance.

• Train customer teams to operate LogIQ independently: run their own demos, onboard new signatures, and interpret RCA outputs.

• Surface recurring pain points and propose product improvements; your customer exposure gives you signal the core product team cannot get from anywhere else.

2+ years of production Python. Comfortable with asyncio, FastAPI, Pydantic v2, and SQLAlchemy 2.0. Ability to read and extend an unfamiliar codebase quickly.

Hands-on experience building or operating LLM-powered agent pipelines — LangChain, LangGraph, CrewAI, AutoGen, or equivalent. Understands state graphs, tool calls, memory, and multi-step reasoning loops.

Can design, implement, and register new agent tools using the @tool decorator pattern (LangGraph/LangChain). Understands tool allowlists, input/output schemas, and safe integration with existing agent contexts.

Can systematically diagnose LLM failure modes and improve prompts through controlled iteration. Understands token budgeting, few-shot construction, output format control, and context window management.

Working knowledge of at least one streaming or log-shipping technology — Kafka, Kinesis, Fluentd, Logstash, syslog-ng, or similar. Understands consumer lag, backpressure, and at-least-once delivery semantics.

Understands async task queues (Celery, SQS, Redis), message broker patterns, and how to debug distributed pipeline failures from logs and traces.

Solid PostgreSQL fundamentals: schema design, JSONB queries, indexing. Exposure to time-series stores (TimescaleDB) and full-text search (OpenSearch / Elasticsearch) is a plus.

Comfortable with AWS (S3, SQS, IAM, Kinesis) or Azure equivalents. Docker and container-based local deployments. Familiarity with docker-compose for multi-service dev environments.

Strong written and spoken English. Can explain a multi-stage agent failure to a non-technical operations director. Experience in customer-facing technical roles — solutions engineering, implementation, pre-sales, or technical consulting — is a strong plus.

Education: B.E. / B.Tech or M.Tech in Computer Science, Electronics, or a related engineering discipline. Equivalent industry experience is fully acceptable.

• Direct experience with LangGraph (our production agent runtime) and the Azure OpenAI SDK.

• Familiarity with multi-tenant SaaS architecture and row-level security (RLS) patterns in PostgreSQL.

• Experience building RAG (retrieval-augmented generation) pipelines — chunking, embedding, retrieval strategies, reranking.

• Knowledge of vector databases or pgvector for semantic search over log and knowledge article corpora.

• TypeScript or Angular familiarity — helpful for front-end troubleshooting and demo customization.

• Domain exposure to telecom (Ciena, Nokia, Ericsson alarms), industrial control systems (SCADA, DCS, PLC events), or large-scale IT infrastructure operations.

• Experience integrating with ITSM tools: ServiceNow, Jira Service Management, PagerDuty, or Salesforce Service Cloud.

• Observability and monitoring experience: Datadog, Grafana, Prometheus — especially for distributed tracing of agent pipelines.

• Open-source contributions, published technical writing, or conference presentations on AI/ML or distributed systems topics.

The engineers who unlock the most value for customers — and grow fastest at Neuron7 — share a distinct profile:

• Full-stack ownership: They own the problem from raw customer log file to production RCA recommendation, without waiting to be handed the next step.

• Diagnostic depth: When an agent misbehaves, they go three levels deep — past the surface symptom into prompt context, retrieval quality, parser correctness, or queue configuration.

• Streaming intuition: They think about live log data as a first-class signal, not an afterthought, and proactively suggest proactive monitoring setups to customers who haven't asked for them yet.

• Tool-builder mindset: When a customer need can't be met with existing tools, they scope, build, and register a new one — and document it well enough that the next customer can benefit.

• Domain curiosity: They ask why a telecom alarm sequence is ordered the way it is, and use that understanding to write better annotations, parsers, and RCA evidence weights.

• Iterative instinct: They treat prompt tuning, retrieval calibration, and anomaly threshold setting as controlled experiments with measurable outcomes.

• Clear communication: They can translate a LangGraph agent failure into a one-paragraph summary that a customer's VP of Operations can act on.

• 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.