Senior AI Solutions Engineer

About Us

is a rapidly growingAIfirstSaaS company focused on building acategorydefiningService Resolution Intelligenceplatform. 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 deliveringaccuraterootcauseanalysis and fix recommendations in secondsleveraginga combination of structured data unstructured data and advanced AI agents. Learn more at.

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 ofLogIQ(reactive and proactive) are expanding fast;youllhelp define how the platformscales tonew 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 worlds most complex operations teams.

• Bangaloreteam with global reach youllwork closely with the US product and engineering leadership giving you visibility and mentorship far beyond a typical India engineering role.

TheRole:

We are hiring AI Solutions Engineers based inBangalorewho will own the full customer journey from first onboarding call to a live productionLogIQdeployment reactiveand 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 howLogIQreduces 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.

WhatYoullDo

We are looking for an Sr. AI Solutions Engineer with 3-5 years of relevant experienceto own the technical customer journey from onboarding and configuration to live production deployments and advanced AI customization.

In this role you will blendstrong software engineering skillshandsonAI/LLM experience andcustomerfacingproblem solving. You will help enterprises operationalizeLogIQby preparing their data configuring AI agents building compelling demos tuning models and ensuring successful outcomes at scale.

Key Responsibilities :

Onboarding & Platform Configuration

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

• Guide customers throughLogIQsSignature 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 KinesisFluentd syslog-ng or customer-native agents intoLogIQsstreaming layer.

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

• Wire streamingtriggers tothe 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 toidentifywhich log sources toprioritize forstreaming vs. batch ingestion balancing latency requirements against infrastructure cost.

3. RCA Agent Configuration & KnowledgeEnrichment

• Ingest and index customer knowledge articles historical case resolutions and equipment documentation into the RCA Agents retrieval layer (OpenSearch pgvector).

• Configure evidence-weightingrulesso 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 customers acceptance criteria.

• Build fix-strategy libraries: map known root causestorecommended 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 customers 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 theirfixvocabulary.

5. Agent Tool & Skill Development

• Design build and register newLangGraphagent tools as customer use cases demand e.g. a tool that queries a customers CMDB pulls ticket history from ServiceNow or fetches firmware changelogs from an internal API.

• Package reusablecapabilitiesasLogIQSkills: self-contained versioned bundles of tools prompts and configuration that can be applied across customers in the same domain.

• Maintaina tool allowlist and review process so new tools integrate safely with the agents execution context and tenant isolation guarantees.

• Contribute high-quality tools back to the platforms shared toollibraryso the whole team benefits.

6. Log Parser & Data Connector Development

• Write custom log parsers for proprietary or undocumented equipment formats (Python plugged into theFastAPIparser registry).

• Build data connectors for customer-specific ingestion sources: REST APIs SFTP drops database exports or cloud storage buckets.

• Define record-splitting rulestypeclassifiers 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 parsersdontregress 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 generationdoesntcover 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 intoLogIQconfiguration and agent guidance.

• Train customer teams to operateLogIQindependently: 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.

CoreRequirements:

Python Engineering-2 years of production Python. Comfortable withasyncioFastAPIPydanticv2 andSQLAlchemy2.0. Ability to read and extend an unfamiliar codebase quickly.

LLM & Agent Frameworks-Hands-on experience building or operating LLM-powered agent pipelines LangChainLangGraphCrewAIAutoGen 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 KinesisFluentd Logstash syslog-ng or similar. Understands consumer lag backpressure and at-least-oncedeliverysemantics.

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-stageagentfailure 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. /orin Computer Science Electronics ora relatedengineering discipline. Equivalent industry experience is fully acceptable.

Nice toHave:

• Direct experience withLangGraph(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 orpgvectorfor 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 contributionspublished technical writing or conference presentations on AI/ML or distributed systems topics.

Education

• Bachelors orMasters degree in Computer Science Engineering ora relatedtechnical 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 behandedthe 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 whohaventasked for them yet.

• Tool-builder mindset: When a customer needcantbe 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 aLangGraphagent failure into a one-paragraph summary that a customers VP of Operations can act on.

What We Do and Value:

At 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: