AI code licensing program assessing existing software repositories for licensing value. Compensating eligible code owners under agreed non - exclusive licensing terms.
About the role
- Senior AI Engineer building reliable, production AI systems for Newfold Digital’s web technology brands. Developing LLM, RAG, agent, workflow, and automation experiences for millions of customers.
Responsibilities
- 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
- Translate product requirements into practical AI solutions using LLMs, RAG, tool calling, agents, deterministic workflows, or traditional software where appropriate
- Build production AI services using Python, FastAPI, asynchronous workers, PostgreSQL, Redis, queues, model APIs, and external tools
- Implement reliable tool-calling agents and multi-step workflows interacting with internal APIs, MCP tools, business systems, and knowledge sources
- Build event-driven workflows using RabbitMQ, Kafka, Azure Service Bus, or equivalent platforms, including retries, dead-letter handling, idempotency, and failure recovery
- Build and improve RAG systems covering ingestion, chunking, embeddings, hybrid retrieval, reranking, metadata filtering, context construction, and citations
- Integrate models from OpenAI, Anthropic, Google, xAI, and open-weight ecosystems; select models based on quality, latency, reliability, and cost
- Build and run AI evaluations using curated datasets, regression tests, retrieval metrics, LLM-as-judge techniques, groundedness checks, and tool-execution evaluation
- Debug hallucinations, retrieval failures, incorrect tool usage, agent loops, latency issues, provider failures, and unexpected inference costs
- Use Cursor, Claude Code, OpenAI Codex, or equivalent coding agents for implementation, testing, debugging, refactoring, and documentation
- Turn product goals into testable technical tasks and prototype alternatives
- Measure quality, latency, reliability, safety, and cost and use those signals to improve solutions
- Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support for assigned features
- Work with senior engineers, architects, and product partners to create reliable, secure, observable, and cost-efficient AI systems
Requirements
- 5 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; 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
- 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
- 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
- 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 contributing to customer-facing AI products, open weight model inference, AI safety controls, automated evaluations, or high-scale backend platforms
Benefits
- No benefits, perks, or compensation extras are specified in the posting
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