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# AI Engineer

- **Company:** J2B Global LLC
- **Location:** Bellevue, WA, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** LangGraph Framework, AI Evaluation, Application Programming Interfaces (APIs), Artificial Intelligence, Amazon Web Services, Microsoft Azure, Cloud Computing, Cloud Engineering, Information Engineering, Data Integration, Python (Programming Language), Knowledge-Based Systems, Operational Data Store, Search Technologies, Software Construction, Software Deployment, Software Engineering, SQL Databases, Systems Integration, Unstructured Data, Enterprise Software Applications, Retrieval-Augmented Generation, Large Language Models, Multi-Agent Systems, Generative AI, Backend, Agentic-AI, Fastapi, Kubernetes, Low Latency, OSS/BSS, Graphql, Machine Learning Operations, Restful APIs, Docker, Microservices
- **Published:** October 27, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=60a08bc038faa0a3

## About the Role

with the FDE-oriented candidate material already being shared with Spectrum, including telecom/broadband operations, GenAI, RAG, LangChain/LangGraph, API integration, vector databases, AI observability and cloud-native deployment. Required Skills Strong hands-on programming in Python GenAI / LLM application development RAG, embeddings, vector databases and semantic search Agentic AI / agent orchestration LangChain and/or LangGraph REST APIs, FastAPI, GraphQL and microservices SQL and data integration AWS, Azure or GCP Docker and Kubernetes CI/CD and software engineering best practices Strong debugging and production troubleshooting skills Excellent customer-facing communication and ability to work with ambiguous requirements Preferred Telecom Experience Cable/Broadband/Telecom domain OSS/BSS platforms Network/service operations Field-service applications and workflows CRM/customer-care platforms Network and operational data AI-powered troubleshooting and knowledge assistants Years of Exp:
8-15+ years of software/data/AI engineering experience, with recent hands-on experience building production GenAI solutions. The person should be a builder first, comfortable sitting with the customer, understanding the problem, coding a prototype quickly, and then taking it into production. Refer

## Description

Job Title: AI Engineer Location: Stamford, CT Duration: Long Term Contract Note: Need candidates from Telecom background only Job Description: We are looking for a hands-on AI Engineer who can work closely with, product, operations, and engineering teams to identify high-value problems and rapidly design, prototype, deploy, and productionize AI/GenAI solutions. The ideal candidate combines strong software engineering skills with Generative AI, agentic AI, data engineering, cloud, APIs, and telecom/broadband domain knowledge. The role requires someone comfortable working directly with customer stakeholders and taking solutions from an ambiguous business problem through production deployment. Key Responsibilities Work directly with stakeholders to understand business and engineering problems and translate them into deployable technical solutions. Rapidly prototype and productionize GenAI/LLM and agentic AI applications. Build RAG-based applications, AI assistants, semantic search, and
enterprise knowledge solutions. Develop agentic workflows using frameworks such as LangChain/LangGraph and integrate agents with enterprise applications and APIs. Build backend services and integrations using Python, REST APIs, GraphQL, FastAPI, SQL and microservices. Integrate AI solutions with telecom platforms, including OSS/BSS, CRM, field-service, network/service operations and enterprise knowledge systems. Work with structured and unstructured data, embeddings, vector databases and enterprise data sources. Implement AI evaluation, observability, guardrails, groundedness checks and hallucination mitigation. Deploy containerized applications using Docker/Kubernetes and cloud platforms such as AWS, Azure or GCP. Troubleshoot customer-environment issues and optimize solutions for reliability, scalability, latency and production readiness. Partner with product and engineering teams and provide feedback from customer deployments to improve reusable solutions. These areas line up strongly