Senior Customer Implementation Engineer

Role Overview: As a Senior Implementation Engineer at Neuron7.ai, you will play a crucial role at the intersection of engineering, AI, and customer delivery. Your primary responsibility will be to develop, integrate, and deploy Python-based backend solutions that power Neuron7's AI-driven resolution platform. This will involve implementing customer-specific workflows, connecting enterprise systems, optimizing data pipelines, and enabling scalable LLM/NLP-powered features in production. You will collaborate closely with Customer Success, ML, Backend Engineering, and PM teams to ensure seamless 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. - Collaborate with ML Engineers to productionize models and optimize performance. - Work with RAG pipelines, embedding workflows, or NLP modules as needed for solution deployment. - 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. Qualification Required: - 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. Additional Company Details (if present): Neuron7.ai is committed to fostering a diverse and inclusive workplace. They prioritize integrity, innovation, and a customer-centric approach. The company values equal employment opportunities without discrimination or harassment based on various factors. Role Overview: As a Senior Implementation Engineer at Neuron7.ai, you will play a crucial role at the intersection of engineering, AI, and customer delivery. Your primary responsibility will be to develop, integrate, and deploy Python-based backend solutions that power Neuron7's AI-driven resolution platform. This will involve implementing customer-specific workflows, connecting enterprise systems, optimizing data pipelines, and enabling scalable LLM/NLP-powered features in production. You will collaborate closely with Customer Success, ML, Backend Engineering, and PM teams to ensure seamless 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. - Collaborate with ML Engineers to productionize models and optimize performance. - Work with RAG pipelines, embedding workflows, or NLP modules as needed for solution deployment. - 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. - Provid