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Neuron 7

AI-powered platform for service resolution

Lead Software Engineer - AL/ML, Implementation Engineer

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeBengaluru, Karnataka, India

About the Job

Requirements

  • At least 7 years of professional experience.
  • Strong Python coding experience.
  • Experience with AI/ML/NLP pipelines, large language models, or retrieval-augmented generation applications.
  • Strong understanding of backend fundamentals, microservices, and distributed systems.
  • Experience with APIs, web frameworks such as FastAPI, Flask, or Django, and RESTful architectures.
  • Hands-on experience with relational and NoSQL databases such as PostgreSQL and MongoDB.
  • Familiarity with cloud platforms such as Azure, AWS, or GCP.
  • Problem-solving and debugging skills.
  • Ability to work cross-functionally with engineering, machine learning, and customer-facing teams.
  • Ability to scope work, sequence delivery, and remove blockers early.
  • Ability to make trade-offs between scope, speed, and quality and adjust plans to protect delivery.
  • Ability to contribute directly in code when progress or clarity depends on it.
  • Ability to codify working patterns into tools, playbooks, or reusable building blocks.
  • Ability to keep teams moving through clarity and follow-through.

Responsibilities

  • Develop and maintain Python-based services, APIs, and integration layers for customer implementations.
  • Build data ingestion, transformation, and validation pipelines to support AI/ML workflows.
  • Implement customer-specific logic, connectors, and automations using Python microservices.
  • Integrate internal machine learning pipelines, large language model components, and retrieval systems into customer environments.
  • Work with retrieval-augmented generation pipelines, embedding workflows, or natural language processing modules as needed for solution deployment.
  • Collaborate with machine learning engineers to productionize models and optimize performance.
  • Configure and deploy services on cloud platforms such as Azure, AWS, or GCP.
  • Ensure reliability, observability, and performance of implementation-specific services.
  • Troubleshoot production issues, analyze logs, and perform root-cause analysis.
  • Work with Customer Success and Solutions teams to translate requirements into technical specifications.
  • Own end-to-end technical implementation for enterprise accounts.
  • Provide guidance on best practices, architecture choices, and scalable patterns.
  • Participate in code reviews and maintain high-quality coding standards.
  • Document implementation workflows, integration steps, and troubleshooting playbooks.
  • Mentor junior team members and contribute to internal tooling and automation.

Desired Qualifications

  • Good knowledge of Java, natural language processing or text processing, or agentic implementation experience.
  • Knowledge of Docker, Kubernetes, and container orchestration.
  • Exposure to message queues such as Kafka or RabbitMQ.
  • Experience with continuous integration/continuous delivery tools and automation frameworks.
  • Prior startup or scale-up experience.
  • Open-source contributions or technical writing experience.

About the Company

Neuron 7

AI-powered platform for service resolution

Neuron7.ai builds an AI-powered service platform that improves outcomes for teams in call centers, tech support, and engineering by guiding issue resolution. It integrates with a client’s existing CRM or service workflows and includes a Smart Resolution Repository that captures and shares knowledge in real time, creating a single system of service intelligence across thousands of products, issues, people, and data silos. The platform works by learning from data and human interactions to diagnose problems and provide step-by-step guidance for resolution. Its approach is solutions-oriented, tailoring the tool to each client’s KPIs and service processes, which has helped customers raise productivity and effectiveness within weeks of deployment. The company’s goal is to help service teams resolve issues faster and more consistently by delivering scalable, data-driven guidance and shared knowledge across the service lifecycle.

Company size
51-200
Company stage
Series B
Total funding
$58.2M
Headquarters
San Jose, CA
Founded
2020

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