About Us:
Neuron7.ai is a fast-growing AI-first SaaS company—Series B funded and trusted by top Fortune 1000 brands—that transforms service resolution with intelligence. Our Smart Resolution Hub delivers predictive, guided troubleshooting across high-tech, medical, and industrial equipment environments. With 300% ARR growth and deep integrations with Salesforce, ServiceNow, Microsoft, and SAP, Neuron7 is scaling rapidly in India and worldwide.
We operate with:
- Integrity and doing business the right way
- Continuous innovation in AI and service intelligence
- Customer obsession with real, measurable impact
- Collective intelligence through collaboration and knowledge sharing
Role Overview
As a Senior Implementation Engineer, you will work at the intersection of engineering, AI, and customer delivery. You’ll build, integrate, and deploy Python-based backend solutions that power Neuron7’s AI-driven resolution platform. This role involves implementing customer-specific workflows, connecting enterprise systems, optimizing data pipelines, and enabling scalable LLM/NLP-powered features in production.
You will partner closely with Customer Success, ML, Backend Engineering, and PM teams to ensure smooth onboarding, robust integrations, and high-quality deployments for enterprise clients.
Key Responsibilities
Python Development & Integrations
- 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.
AI/ML & NLP (Implementation Focus)
- Integrate internal ML pipelines, LLM components, and retrieval systems into customer environments.
- Work with RAG pipelines, embedding workflows, or NLP modules as needed for solution deployment.
- Collaborate with ML Engineers to productionize models and optimize performance.
Deployment & System Reliability
- Configure and deploy services on cloud platforms (Azure/AWS/GCP).
- Ensure reliability, observability, and performance of implementation-specific services.
- Troubleshoot production issues, analyze logs, and perform root-cause analysis.
Customer Delivery & Collaboration
- Work closely with Customer Success & 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.
Team Excellence
- 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.
Required Qualifications
- Total 5+ years of professional experience.
- 2+ years of Python coding experience.
- Strong understanding of backend fundamentals, microservices, and distributed systems.
- Experience working with APIs, web frameworks (FastAPI, Flask, Django), and RESTful architectures.
- Hands-on experience with relational & NoSQL databases (PostgreSQL, MongoDB, or similar).
- Familiarity with cloud platforms (Azure, AWS, or GCP).
- Strong problem-solving, debugging, and communication skills.
- Ability to work cross-functionally with engineering, ML, and customer-facing teams.
- Scope work, sequence delivery, and remove blockers early.
- Make trade-offs between scope, speed, and quality; adjust plans to protect delivery.
- Contribute directly in the code when progress or clarity depends on it.
- Codify working patterns into tools, playbooks, or building blocks that others can use.
- Keep teams moving through clarity and follow-through.
Desired (Nice-to-Have)
- Experience with AI/ML/NLP pipelines, LLMs, or RAG-based applications.
- Good knowledge in Java, NLP or text processing or agentic implementation experience.
- Knowledge of Docker, Kubernetes, and container orchestration.
- Exposure to message queues (Kafka, RabbitMQ).
- Experience with CI/CD tools and automation frameworks.
- Prior startup or scale-up experience.
- Open-source contributions or technical writing experience.