Senior Applied AI Engineer
What you’ll do & how you’ll make your mark .
We are looking for ahandon Senior Applied AI Engineer who combines strong software engineering with practical AI problem solving. You will build production AI experiences across Network Solutions, including conversational agents, business and website creation, knowledge and FAQ agents, content experiences, domain discovery, and intelligent automation.
You will take ambiguous customer and product problems, evaluate different approaches, and independently turn the strongest solution into reliable, secure, observable, andcost-efficientproduction systems. This is an applied engineering role focused on shipping real AI products.
Who you are & what you’ll need to succeed
Design and build production AI services using Python,FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools.
Build reliable tool calling agents andmulti stepworkflows that interact with internal APIs, MCP tools, business systems, and knowledge sources.
Design event driven systems using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including retries, dead letter handling, idempotency, back pressure, and failure recovery.
Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations.
Experiment with models from OpenAI, Anthropic, Google,xAI, and open weight ecosystems, selecting the right model for quality, latency, reliability, and cost.
Create AI evaluation pipelines using curated datasets, regression tests, retrieval metrics, LLM as judge techniques,groundednesschecks, and tool execution evaluation.
Diagnose hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs.
Operate as an AI powered engineer using Cursor, Claude Code, OpenAI Codex, or equivalent coding agents to accelerate design, implementation, testing, debugging, and refactoring.
Whatwe’relooking for
5 or more years of professional software engineering experience building production backend, distributed, orcloud basedsystems.
Hands on experience building Applied AI, LLM, RAG, NLP, oragent basedapplications, with meaningful production exposure.
Advanced Python skills including
FastAPI, asynchronous programming,Pydantic,SQLAlchemyorSQLModel, andproductionAPI development.
Strong backend and distributed systems fundamentals including REST APIs, concurrency, background processing, caching, reliability, and production debugging.
Production experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging architectures.
Hands onexperience integrating LLM APIs and building structured output, function calling, tool calling, or agent execution workflows.
Experience building at least one RAG or knowledge grounded system with measurable quality and latency outcomes.
Strong PostgreSQL and data modeling skills, with experience using Redis,pgvector, vector databases, or hybrid search technologies.
Demonstrated use of AI coding agents such as Cursor, Claude Code, Codex, or equivalent tools as a core part of daily software engineering.
Nice to have.
You should be comfortable going beyond a working demo andunderstandhow modern AI systems behave, fail, and scale in production.
Strong understanding of prompting, structured outputs, tool schemas, context management, model routing, retries, fallbacks, rate limits, and streaming responses.
Ability to design experiments and evaluations that compare approaches using measurable…
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