Customer Implementation Engineer
Job description
About the role
You own the end-to-end technical delivery of Neuron7 solutions in customer environments, ensuring that AI-driven resolution capabilities are implemented reliably and meet enterprise standards. You will design and deploy Python-based services and integration layers that connect critical business systems to the Smart Resolution Hub. This role requires you to translate complex customer requirements into scalable technical specifications while collaborating across Customer Success, ML, and backend teams. You will build data pipelines, implement customer-specific workflows, and optimize the performance of production services. You play a key role in enabling LLM and NLP capabilities in live deployments and ensuring high reliability. You will mentor peers, document implementation patterns, and contribute to internal tooling that accelerates future implementations. Ultimately, you ensure that each customer deployment delivers measurable value and reinforces trust in Neuron7's platform.
Key facts
What you'll do
Orchestrate the design, development, and deployment of Python-based services that power Neuron7's resolution platform in customer environments.
Build and maintain robust data ingestion, transformation, and validation pipelines to support AI and machine learning workflows.
Implement customer-specific business logic, connectors, and automations using Python microservices and integration patterns.
Integrate internal ML pipelines, LLM components, and retrieval systems to enable RAG, embedding workflows, and NLP modules in production.
Collaborate closely with ML engineers to productionize models, optimize inference performance, and ensure scalability under load.
Configure, deploy, and operate services across major cloud platforms including Azure, AWS, and GCP with attention to reliability and security.
Ensure implementation services exhibit high observability, perform monitoring, and enable rapid troubleshooting of production issues.
Analyze logs, trace failures, and perform root-cause analysis to resolve complex issues affecting customer deployments.
Work directly with Customer Success and Solutions teams to convert business requirements into precise technical specifications.
Own the complete technical implementation for enterprise accounts, including scoping, sequencing, and on-time delivery of milestones.
Provide guidance on architecture best practices, scalable patterns, and integration strategies to internal and external stakeholders.
Participate in rigorous code reviews to uphold coding standards, security practices, and overall implementation quality.
Document integration steps, deployment procedures, and troubleshooting playbooks to ensure continuity and repeatability.
Mentor junior engineers, share knowledge, and contribute to internal tools and automation that improve team efficiency.
Enable cross-functional collaboration to remove blockers early, manage scope trade-offs between speed, quality, and delivery commitments.
Requirements
Bring a minimum of 3 years of total professional experience in software engineering or technical implementation roles.
Possess 2 or more years of hands-on Python development experience with a strong portfolio of real-world projects.
Demonstrate a deep understanding of backend fundamentals, microservices architecture, and distributed system design principles.
Show proven ability to work with RESTful APIs, web frameworks such as FastAPI, Flask, and Django, and standard HTTP patterns.
Have hands-on experience with both relational databases like PostgreSQL and NoSQL databases such as MongoDB.
Exhibit familiarity with major cloud platforms including Azure, AWS, or GCP and their core services.
Strongly demonstrate problem-solving, debugging, and communication skills across technical and non-technical audiences.
Consistently work cross-functionally with engineering, machine learning, and customer-facing teams to align on goals.
Effectively scope work, sequence delivery activities, and proactively identify and remove blockers before they impact timelines.
Make informed trade-offs between scope, speed, and quality while protecting the integrity of customer deliveries.
Be prepared to contribute directly to code when critical decisions depend on implementation clarity or technical insight.
Codify successful working patterns into reusable tools, playbooks, and building blocks that other teams can adopt.
Ensure consistent follow-through and clarity in communication to keep multiple teams aligned and moving efficiently.
Commit to maintaining high standards of quality in implementation work and adherence to defined processes.
Nice to have
Prior experience with AI, ML, or NLP pipelines, including work with LLMs or RAG-based applications in production.
Strong knowledge of Java or extensive experience with NLP or text processing and agentic implementation patterns.
Hands-on exposure to Docker, Kubernetes, and container orchestration for deploying resilient services.
Background with message queues such as Kafka or RabbitMQ for building asynchronous integration flows.
Experience with CI/CD tools, automation frameworks, and infrastructure-as-code practices.
Having worked in a startup or scale-up environment where agility and ownership were essential.
Contributed to open-source projects or produced technical writing that demonstrates clear communication skills.
Practical notes
The role is based in Bangalore and offers a flexible hybrid working arrangement.
Output: 744 words
About the role
You own the end-to-end technical delivery of Neuron7 solutions in customer environments, ensuring that AI-driven resolution capabilities are implemented reliably and meet enterprise standards. You will design and deploy Python-based services and integration layers that connect critical business systems to the Smart Resolution Hub. This role requires you to translate complex customer requirements into scalable technical specifications while collaborating across Customer Success, ML, and backend teams. You will build data pipelines, implement customer-specific workflows, and optimize the performance of production services. You play a key role in enabling LLM and NLP capabilities in live deployments and ensuring high reliability. You will mentor peers, document implementation patterns, and contribute to internal tooling that accelerates future implementations. Ultimately, you ensure that each customer deployment delivers measurable value and reinforces trust in Neuron7's platform.
Key facts
What you'll do
Orchestrate the design, development, and deployment of Python-based services that power Neuron7's resolution platform in customer environments.
Build and maintain robust data ingestion, transformation, and validation pipelines to support AI and machine learning workflows.
Implement customer-specific business logic, connectors, and automations using Python microservices and integration patterns.
Integrate internal ML pipelines, LLM components, and retrieval systems to enable RAG, embedding workflows, and NLP modules in production.
Collaborate closely with ML engineers to productionize models, optimize inference performance, and ensure scalability under load.
Configure, deploy, and operate services across major cloud platforms including Azure, AWS, and GCP with attention to reliability and security.
Ensure implementation services exhibit high observability, perform monitoring, and enable rapid troubleshooting of production issues.
Analyze logs, trace failures, and perform root-cause analysis to resolve complex issues affecting customer deployments.
Work directly with Customer Success and Solutions teams to convert business requirements into precise technical specifications.
Own the complete technical implementation for enterprise accounts, including scoping, sequencing, and on-time delivery of milestones.
Provide guidance on architecture best practices, scalable patterns, and integration strategies to internal and external stakeholders.
Participate in rigorous code reviews to uphold coding standards, security practices, and overall implementation quality.
Document integration steps, deployment procedures, and troubleshooting playbooks to ensure continuity and repeatability.
Mentor junior engineers, share knowledge, and contribute to internal tools and automation that improve team efficiency.
Enable cross-functional collaboration to remove blockers early, manage scope trade-offs between speed, quality, and delivery commitments.
Requirements
Bring a minimum of 3 years of total professional experience in software engineering or technical implementation roles.
Possess 2 or more years of hands-on Python development experience with a strong portfolio of real-world projects.
Demonstrate a deep understanding of backend fundamentals, microservices architecture, and distributed system design principles.
Show proven ability to work with RESTful APIs, web frameworks such as FastAPI, Flask, and Django, and standard HTTP patterns.
Have hands-on experience with both relational databases like PostgreSQL and NoSQL databases such as MongoDB.
Exhibit familiarity with major cloud platforms including Azure, AWS, or GCP and their core services.
Strongly demonstrate problem-solving, debugging, and communication skills across technical and non-technical audiences.
Consistently work cross-functionally with engineering, machine learning, and customer-facing teams to align on goals.
Effectively scope work, sequence delivery activities, and proactively identify and remove blockers before they impact timelines.
Make informed trade-offs between scope, speed, and quality while protecting the integrity of customer deliveries.
Be prepared to contribute directly to code when critical decisions depend on implementation clarity or technical insight.
Codify successful working patterns into reusable tools, playbooks, and building blocks that other teams can adopt.
Ensure consistent follow-through and clarity in communication to keep multiple teams aligned and moving efficiently.
Commit to maintaining high standards of quality in implementation work and adherence to defined processes.
Nice to have
Prior experience with AI, ML, or NLP pipelines, including work with LLMs or RAG-based applications in production.
Strong knowledge of Java or extensive experience with NLP or text processing and agentic implementation patterns.
Hands-on exposure to Docker, Kubernetes, and container orchestration for deploying resilient services.
Background with message queues such as Kafka or RabbitMQ for building asynchronous integration flows.
Experience with CI/CD tools, automation frameworks, and infrastructure-as-code practices.
Having worked in a startup or scale-up environment where agility and ownership were essential.
Contributed to open-source projects or produced technical writing that demonstrates clear communication skills.
Practical notes
The role is based in Bangalore and offers a flexible hybrid working arrangement.