Jobs at Neuron7.ai

Entry-level frontend role building responsive, accessible UI features from Figma designs. Implement pixel-accurate components, debug cross-browser issues, document components, and collaborate with designers and backend engineers to ship features end-to-end.

The Product Manager will lead the strategy and execution of agent product experiences, collaborating with teams to enhance AI diagnostics and optimize service workflows.

Lead and scale a global engineering organization to deliver enterprise-grade AI/GenAI SaaS products. Define technology strategy and roadmap, drive architecture for LLMs/RAG/NLP systems, ensure scalability/reliability, establish DevOps and quality practices, partner with Product/Sales/Customer teams, and manage hiring, budgets, and leadership development.

Design and deliver enterprise-grade AI/GenAI solutions on Salesforce or ServiceNow. Lead RAG pipelines, LLM workflows, NLP automation, and vector retrieval integrations. Architect scalable API/microservice integrations, ensure performance, security, and reliability, and define AI governance and guardrails. Collaborate with product, engineering, and customers through discovery, workshops, and demos to operationalize AI for Field and Customer Service workflows.

The Release Manager will oversee the complete release lifecycle, emphasizing QA processes, CI/CD pipeline optimization, and effective communication with stakeholders to ensure high-quality software releases.

Lead customer-facing technical implementations of enterprise AI solutions: design architectures, build integrations and scalable backend services (Java/Python), configure RAG and AI agents, troubleshoot production issues, mentor engineers, and collaborate across product, AI research, and customer success to deliver and optimize deployments.

Provide high-level administrative support to senior leaders: manage complex international calendars, coordinate meetings and travel, prepare agendas and materials, track project milestones and action items, coordinate cross-functional initiatives and events, handle confidential communications, and improve administrative processes to increase operational efficiency.

Implement and deploy Python-based backend services and integrations for enterprise customers, build data pipelines for AI/ML workflows, integrate LLM/NLP and RAG components, ensure reliable cloud deployments, troubleshoot production issues, and collaborate with Customer Success, ML, and engineering teams to deliver scalable, productionized solutions.

Lead end-to-end technical implementations for enterprise customers: develop Python microservices and APIs, build data ingestion and validation pipelines, integrate LLM/NLP and ML components (RAG/embeddings), deploy services to cloud, ensure reliability and observability, troubleshoot production issues, and collaborate with Customer Success and ML teams while mentoring junior engineers.

Lead architecture and development of scalable backend and full-stack features for AI-native products, build agentic AI workflows and orchestration, design distributed systems and APIs, ensure security and performance, troubleshoot production, and mentor engineers.

As a Staff Data Scientist, lead the design and deployment of NLP solutions, mentor team members, and collaborate on AI/ML products to analyze unstructured data.

The role involves managing customer onboarding, building demos, and configuring AI solutions as part of the deployment process. The engineer will develop streaming log ingestion, enhance the RCA agent, and contribute to tool and log parser development, ensuring successful AI operationalization for enterprise customers.

Design and build Python-based integrations, data pipelines, APIs, and AI-enabled workflows; deploy and maintain LLM/NLP/RAG capabilities in cloud environments; troubleshoot production issues; lead customer-facing implementations and mentor engineers to deliver scalable, reliable solutions.

Lead implementation of Python-based backend integrations and data pipelines to deploy LLM/NLP/RAG solutions for enterprise clients. Configure cloud deployments, ensure reliability and observability, troubleshoot production issues, and collaborate with Customer Success, ML, and engineering teams. Mentor juniors and document implementation playbooks.

Design, implement, and manage CI/CD pipelines and cloud infrastructure (AWS/Azure/GCP); automate provisioning with Terraform/Ansible; containerize apps with Docker/Kubernetes; monitor performance (Prometheus/Grafana/ELK); ensure security/compliance; troubleshoot availability issues; mentor junior engineers and document processes.

The role involves guiding the implementation and optimization of AI solutions for enterprise clients, focusing on customer onboarding, technical deployments, and service transformation initiatives.

The Product Management Apprentice supports product teams in building AI-powered products by assisting in documenting requirements, analyzing use cases, and collaborating across teams, offering hands-on training for early-career talent.