Implementation Engineer

Role Overview

• Work at the intersection of engineering, AI, and customer delivery.
• Build, integrate, and deploy Python-based backend solutions that power Neuron7’s AI-driven resolution platform.
• Implement customer-specific workflows, connecting enterprise systems, optimizing data pipelines, and enabling scalable LLM/NLP-powered features in production.
• Partner closely with Customer Success, ML, Backend Engineering, and PM teams for smooth onboarding and robust 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.
• 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.
• 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.
• Own end-to-end technical implementation for enterprise accounts.
• Provide guidance on best practices, architecture choices, and scalable patterns.
• Document implementation workflows, integration steps, and troubleshooting playbooks.
• Participate in code reviews and maintain high-quality coding standards.
• Contribute to internal tooling and automation.

Requirements

• 2–5 years of professional experience in Python development.
• 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.
• Experience with AI/ML/NLP pipelines, LLMs, or RAG-based applications (Desired).
• Good knowledge in Java, NLP or text processing or agentic implementation experience (Desired).
• Knowledge of Docker, Kubernetes, and container orchestration (Desired).
• Exposure to message queues (Kafka, RabbitMQ) (Desired).
• Experience with CI/CD tools and automation frameworks (Desired).
• Prior startup or scale-up experience (Desired).
• Open-source contributions or technical writing experience (Desired).

Tech Stack

• AWS
• Azure
• Cloud
• Distributed Systems
• Django
• Docker
• Flask
• Google Cloud Platform
• Java
• Kafka
• Kubernetes
• Microservices
• MongoDB
• NoSQL
• Postgres
• Python
• RabbitMQ

Benefits

• Competitive salary, equity
• Paid Time-off
• Comprehensive health insurance.
• Paid parental leave.
• Flexible hybrid work.