Senior Applied AI Engineer

Network Solutions; Argentina; Presencial; ARS 1.800.000 - 3.000.000; Jornada completa

Descripción de la vacante

Network Solutions is seeking a hands-on 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

Formación

• 5+ years of professional software engineering experience building production backend, distributed, or cloud-based systems.
• Hands-on experience building Applied AI, LLM, NLP, or agent-based applications, with meaningful production exposure.
• Advanced Python skills including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API 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 on experience 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.

Responsabilidades

• Translate loosely defined product problems into practical AI solutions, choosing between LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software.
• Design and build production AI services using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools.
• Build reliable tool calling agents and multi step workflows 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, groundedness checks, 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.
• Turn product goals into testable technical hypotheses and rapidly prototype alternatives when needed.
• Define quality, latency, reliability, safety, and cost targets and measure production performance against them.
• Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support.
• Use AI coding agents aggressively to increase engineering velocity while preserving architecture quality, testing discipline, security, and maintainability.

Conocimientos

Production backend development

Applied AI / LLM / NLP

Python programming

REST APIs design

Distributed systems fundamentals

Observability & debugging

Herramientas

PostgreSQL

Redis

RabbitMQ

Kafka

Azure Service Bus

OpenTelemetry

Langfuse

Cursor

Claude Code

OpenAI Codex

Descripción del empleo

At Network Solutions, we’ve been trusted for decades to help people get online and stay ahead. We’ve been here since the beginning of the internet, and we’re still building for what comes next.

As the original digital identity authority, we help secure domain names, protect brands, and safeguard the infrastructure businesses rely on. We empower our customers to own and manage the assets that define them online, while delivering enterprise-grade security to protect against virtual threats. Our team leverages modern, AI-accelerated tools to streamline how businesses manage their digital presence, making the most of our decades of experience.

The Network Solutions team is here to help online businesses protect what’s theirs and build for tomorrow. That’s why millions trust us to protect their domains, brands, and websites every day.

The impact you’ll make

We are looking for a hands on 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, and cost efficient production systems. This is an applied engineering role focused on shipping real AI products.

What you’ll do

• Translate loosely defined product problems into practical AI solutions, choosing between LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software.
• Design and build production AI services using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools.
• Build reliable tool calling agents and multi step workflows 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, groundedness checks, 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.

What we’re looking for

• 5 or more years of professional software engineering experience building production backend, distributed, or cloud based systems.
• Hands on experience building Applied AI, LLM, RAG, NLP, or agent based applications, with meaningful production exposure.
• Advanced Python skills including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API 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 on experience 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.

Applied AI engineering knowledge

You should be comfortable going beyond a working demo and understand how 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 outcomes rather than subjective testing alone.
• Understanding of retrieval quality, grounding, hallucination mitigation, prompt injection risks, tool authorization, and practical agent guardrails.
• Experience with Docker, CI/CD, automated testing, observability, distributed tracing, OpenTelemetry, Langfuse, or similar tooling.

How you’ll work

You will often start with an ambiguous customer or product goal rather than a detailed implementation plan. You should be able to independently break the problem down, validate assumptions, choose an approach, and drive the solution through production.

• Turn product goals into testable technical hypotheses and rapidly prototype alternatives when needed.
• Choose the simplest architecture that solves the problem well rather than using an agent or LLM by default.
• Define quality, latency, reliability, safety, and cost targets and measure production performance against them.
• Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support.
• Use AI coding agents aggressively to increase engineering velocity while preserving architecture quality, testing discipline, security, and maintainability.

What will make you stand out

• Experience with Semantic Kernel, Microsoft Agent Framework, LangGraph, PydanticAI, or equivalent agent frameworks.
• Experience building or consuming Model Context Protocol tools and servers, or working with model gateways such as LiteLLM.
• Experience with Azure AI Search, pgvector, Chroma, Kubernetes, Azure, OCI, or similar production AI platform technologies.
• Experience building high scale customer facing AI products, open weight model inference, AI safety controls, or automated agent evaluation systems.

Strong problem solving, excellent engineering judgment, and the ability to independently move from ambiguity to a production quality solution are the foundation for this role.