Lead Software Engineer
Job description
About the role
You will own the design and delivery of Python-based backend services that power Neuron7's AI-first resolution platform. You will architect and implement customer-specific integration workflows that connect enterprise data systems with our Smart Resolution Hub. You will lead the implementation of AI and NLP capabilities in production, ensuring reliability and performance at enterprise scale. You will act as a technical owner for end-to-end customer deployments, collaborating across teams to translate business needs into robust software solutions. You will mentor junior engineers, elevate code quality, and drive best practices across the implementation lifecycle. You will troubleshoot complex production issues and optimize data pipelines for speed and stability. You will contribute directly to architecture decisions that shape how customers experience Neuron7's platform.
Key facts
What you'll do
Architect and develop scalable Python microservices that serve as the backbone of customer implementations.
Design and build data ingestion, transformation, and validation pipelines to feed AI and ML workflows reliably.
Implement customer-specific business logic, connectors, and automations aligned with enterprise requirements.
Integrate internal ML pipelines, LLM components, and retrieval systems into production environments.
Work with RAG pipelines, embedding workflows, and NLP modules to enable advanced resolution capabilities.
Collaborate with ML engineers to productionize models, optimize inference, and ensure robust performance.
Configure and deploy services across cloud platforms including Azure, AWS, and GCP.
Ensure high reliability, observability, and performance of implementation-specific services in production.
Troubleshoot production incidents, analyze logs, and perform deep root-cause analysis for critical issues.
Partner with Customer Success and Solutions teams to convert complex requirements into technical specifications.
Own the technical implementation for enterprise accounts from design through deployment and stabilization.
Provide guidance on architecture choices, scalable patterns, and best practices to internal and external stakeholders.
Lead code reviews and maintain high coding standards to ensure maintainable and resilient systems.
Document implementation workflows, integration steps, and troubleshooting playbooks for consistency.
Mentor junior team members and contribute to internal tooling that improves team efficiency.
Requirements
Total 7+ years of professional experience in software engineering or related roles.
Strong Python coding experience with demonstrated ability to write clean, maintainable, and scalable code.
Experience with AI, ML, and NLP pipelines, including work with LLMs or RAG-based applications.
Solid understanding of backend fundamentals, microservices architecture, and distributed system design.
Hands-on experience with RESTful APIs, web frameworks such as FastAPI, Flask, and Django, and API integration patterns.
Proficiency with relational and NoSQL databases including PostgreSQL, MongoDB, and similar technologies.
Familiarity with major cloud platforms such as Azure, AWS, or GCP and their core services.
Strong problem-solving skills, debugging capabilities, and effective communication across technical and non-technical audiences.
Proven ability to work cross-functionally with engineering, ML, and customer-facing teams in a collaborative manner.
Capacity to scope work, sequence delivery milestones, and remove blockers early to protect timelines.
Willingness to make trade-offs between scope, speed, and quality while maintaining accountability for delivery.
Ability to contribute directly in the codebase when progress or clarity depends on implementation details.
Commitment to codifying working patterns into tools, playbooks, and building blocks that other engineers can reuse.
Dedication to keeping teams moving forward through clarity, follow-through, and consistent execution.
Nice to have
Good knowledge of Java and experience with NLP or text processing frameworks.
Prior experience with agentic implementation patterns and complex workflow orchestration.
Knowledge of Docker, Kubernetes, and container orchestration for deployment and scaling.
Exposure to message queues such as Kafka or RabbitMQ for event-driven architectures.
Experience with CI/CD tools and automation frameworks to streamline delivery.
Prior startup or scale-up experience in fast-paced, high-growth environments.
Open-source contributions or technical writing experience that demonstrates communication and clarity.
Practical notes
The role is based in Bangalore with flexible hybrid working arrangements.
No specific working hours are mandated, but the position requires availability during standard business hours for collaboration.
There is no travel requirement specified for this position.
No visa sponsorship information is provided in the source material.
No application deadline is communicated in the source material.