Want to know if this job is worth applying to?
Bengaluru, Karnataka, India
Onsite
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 — you are a contributor, not just a consumer.
Debug async pipeline failures.
8. Customer Partnership & Knowledge Transfer
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.
Core Requirements:
Python Engineering - 2+ years of production Python. Comfortable with asyncio, FastAPI, Pydantic v2, and SQLAlchemy 2.0. Ability to read and extend an unfamiliar codebase quickly.
LLM & Agent Frameworks - 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.
Agent Tool Development - 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.
Prompt Engineering - 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.
Streaming & Event Systems - 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.
Async & Distributed Systems - Understands async task queues (Celery, SQS, Redis), message broker patterns, and how to debug distributed pipeline failures from logs and traces.
Databases & Search - Solid PostgreSQL fundamentals: schema design, JSONB queries, indexing. Exposure to time-series stores (TimescaleDB) and full-text search (OpenSearch / Elasticsearch) is a plus.
Cloud & Infrastructure - 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.
Customer Communication - 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 Qualification: B.E. / B.Tech or M.Tech in Computer Science, Electronics, or a related engineering discipline. Equivalent industry experience is fully acceptable.
Nice to Have :
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.
Education
Bachelor’s or Master’s degree in Computer Science, Engineering, or a related technical field, or equivalent industry experience.
What 'Great' Looks Like in This Role
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.
What We Do and Value:
At Neuron7.ai, we prioritize integrity, innovation, and a customer-centric approach. Our mission is to enhance service decision-making through advanced AI technology, and we are dedicated to delivering excellence in all aspects of our work.
Company Perks & Benefits:
Competitive salary, equity, and spot bonuses.
Paid sick leave.
Comprehensive health insurance.
Paid parental leave.
Flexible work arrangements.
Our Commitment to Diversity and Inclusion:
Neuron7.ai is committed to fostering a diverse and inclusive workplace. We ensure equal employment opportunities without discrimination or harassment based on race, color, religion, sex (including pregnancy, childbirth, or related medical conditions), sexual orientation, gender identity or expression, age, disability, national origin, marital status, or any other characteristic protected by law.