[Hiring] Software Engineer @Newfold Digital

Role Description

We are looking for a hands-on Applied AI Engineer who combines strong software engineering fundamentals with practical AI problem solving. You will help build production AI experiences across Network Solutions, including:

• Conversational agents
• Business and website creation
• Knowledge and FAQ agents
• Content experiences
• Domain discovery
• Intelligent automation

You will work with senior engineers, architects, and product partners to turn customer and product problems into reliable, secure, observable, and cost-efficient AI systems. This is an applied engineering role focused on building, measuring, and continuously improving real AI products.

Qualifications

• 3 or more years of professional software engineering experience building production backend, distributed, or cloud-based systems.
• At least 1 year of hands-on experience building Applied AI, LLM, RAG, NLP, or agent-based applications, with production experience strongly preferred.
• Strong Python skills including FastAPI, asynchronous programming, Pydantic, SQLAlchemy or SQLModel, and production API development.
• Good backend and distributed systems fundamentals including REST APIs, concurrency, background processing, caching, reliability, and production debugging.
• Hands-on experience with RabbitMQ, Kafka, Azure Service Bus, or equivalent queue and messaging architectures is required.
• Hands-on experience integrating LLM APIs and building structured output, function calling, tool calling, or agent execution workflows.
• Experience building or contributing to a RAG or knowledge grounded system, including retrieval, embeddings, indexing, context construction, and evaluation.
• Working knowledge of PostgreSQL and data modeling, with exposure to 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 regular part of software engineering work.

Requirements

• You should be comfortable going beyond a working demo and understand the core engineering patterns needed to make AI systems reliable in production.
• Understanding of prompting, structured outputs, tool schemas, context management, retries, fallbacks, rate limits, caching, and streaming responses.
• Ability to use experiments and evaluations to 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, logging, observability, distributed tracing, OpenTelemetry, Langfuse, or similar tooling.

Benefits

• Join us to build the agent-powered backbone of our AI platform—robust, model agnostic, and ready for millions of users.