Neuron7.ai
Website:
neuron7.ai
Job details:
AI Solutions Engineer: Customer Onboarding, Streaming AI & Custom Demos
About Neuron7 & LogIQ
Neuron7.ai builds AI-powered resolution intelligence platforms that help enterprises resolve complex operational issues faster. LogIQ is our flagship product: a multi-tenant, agentic log analysis platform used by telecom, industrial controls, and IT infrastructure customers worldwide.
LogIQ operates in two complementary modes:
- Reactive Analysis: Customer uploads a log bundle (a case). LogIQ's Symptom Detection engine runs active signatures against the logs to surface alarms, anomalies, and deviations. The RCA Agent then performs evidence-based reasoning across the log bundle, historical cases, and knowledge articles to identify the true root cause and recommend the optimal fix strategy.
- Proactive / Streaming Analysis: LogIQ ingests a live log stream and monitors it continuously. The Anomaly Detection engine watches for deviations from healthy patterns, known issue signatures, and emerging fault sequences. When a trigger fires, the same RCA Agent is invoked automatically to investigate and surface a root-cause + fix recommendation in real time — before the customer even opens a ticket.
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
- Customer Onboarding & Environment Setup
- Streaming Log Ingestion & Proactive Monitoring
- RCA Agent Configuration & Knowledge Enrichment
- Custom Demo Engineering
- Agent Tool & Skill Development
- Log Parser & Data Connector Development
- Platform Customization & Code Contributions
- Customer Partnership & Knowledge Transfer
Required Skills & Qualifications
Skill Area What We Expect
- Python Engineering ,3+ 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: 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.
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.
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.
Click on Apply to know more.