Newfold Digital

Account Executive

• Conduct outbound prospecting using tools such as 6sense and Outreach.io
• Identify and target digital agencies and freelancers
• Facilitate onboarding of new agencies onto the Bluehost platform
• Build and maintain long-term relationships with digital agencies
• Analyze agency business models and client types to identify sales opportunities
• Manage a portfolio of agencies and grow adoption and revenue contribution
• Manage the full sales cycle from prospecting and closing to post-sale account management
• Meet and exceed customer satisfaction and daily sales targets
• Resolve customer issues within the support scope
• Multitask and prioritize in a high-volume, dynamic environment
• Understand customer needs and close sales ethically
• Communicate persuasively with stakeholders
• Help small and medium-sized businesses succeed

Agency Sales Account Executive

• Lead a high-velocity outbound sales motion targeting small and medium-sized businesses
• Drive net-new business through high-volume cold calling, email, and social outreach
• Identify, target, and engage new prospects with no prior relationship to Bluehost
• Assess customer needs, qualify opportunities, and move prospects through the funnel
• Own the full sales cycle from first touch through close, including discovery, objection handling, and deal execution
• Acquire new customers and expand Bluehost’s footprint in the SMB market
• Educate prospects on how Bluehost solutions support their online presence and growth
• Meet daily and weekly activity metrics, including calls, meetings set, pipeline generated, and MRR targets
• Navigate competitive conversations and close deals with urgency and integrity

Frontend Engineer

• Architect and build high-performance, production-grade user interfaces with React, Redux or similar state management, and TypeScript
• Use AI across the delivery lifecycle and establish guardrails for generated-code review, provenance, licensing, and customer-data prompts
• Design and build front-end interfaces integrating AI-powered features such as chat, content generation, and smart recommendations
• Own performance, scalability, and reliability of high-traffic customer-facing applications
• Partner with product, design, and backend engineering teams to take features from concept to production
• Set and enforce frontend engineering standards for component reuse, design-system consistency, testing coverage, and code review
• Mentor mid-level and junior engineers on architecture, debugging, and modern React patterns
• Drive continuous delivery and iterative releases based on real user feedback

Senior Frontend Engineer

• Design and scale async REST/WebSocket APIs in Python using FastAPI or similar, dependency injection, type hints, and clean vertical-slice architecture
• Implement sequential, concurrent, and handoff multi-agent workflows among specialized LLM agents
• Integrate multiple LLM providers behind a provider-agnostic layer for A/B testing and cost-aware routing
• Build and maintain RAG pipelines using vector stores such as pgvector, Chroma, or managed vector search services
• Design schemas and evolve data models using SQLAlchemy/SQLModel-style ORM and migrations
• Instrument services with structured logs, distributed tracing, and cost/latency metrics
• Maintain end-to-end CI/CD ownership, including linting, type-checking, testing, packaging, and deployment
• Champion AI across the delivery lifecycle and establish guardrails for code review, provenance, licensing, and customer-data use
• Move ideas from concept to production with ownership of architecture and outcomes

• Architect and build high-performance, production-grade UI in React, Redux or similar state management, and TypeScript
• Use AI across the delivery lifecycle and establish guardrails for generated-code review, provenance, licensing, and customer-data prompts
• Design and build front-end interfaces integrating AI-powered features such as chat, content generation, and smart recommendations
• Own performance, scalability, and reliability of high-traffic, customer-facing applications
• Partner with product, design, and backend engineers to take features from concept to production quickly
• Set and enforce frontend engineering standards for component reuse, design-system consistency, testing coverage, and code review rigor
• Mentor mid-level and junior engineers on architecture, debugging, and modern React patterns
• Drive continuous delivery practices and ship iteratively based on real user feedback

AI Engineer

• 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 loosely defined product problems into practical AI solutions
• Evaluate different approaches and independently turn the strongest solution into reliable, secure, observable, and cost-efficient production systems
• 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 interacting with internal APIs, MCP tools, business systems, and knowledge sources
• Design event-driven systems with 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
• 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
• Use AI coding agents to accelerate design, implementation, testing, debugging, and refactoring
• Turn product goals into testable technical hypotheses and rapidly prototype alternatives
• Define quality, latency, reliability, safety, and cost targets and measure production performance
• Own implementation across APIs, workflows, data, queues, model integration, evaluations, observability, and production support

Senior Software Engineer – AI

• 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 with 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
• 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
• Break product goals into manageable pieces, validate assumptions, implement solutions, and discuss architecture tradeoffs with senior engineers
• 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
• Use AI coding agents to increase engineering velocity while preserving testing discipline, security, maintainability, and code quality