PulseRise Technologies

• Own growth end-to-end across: Influencer marketing, Performance marketing, Organic content • Take over and scale existing initiatives currently run by founder and contractors • Design and launch new growth experiments and channels • Drive measurable customer acquisition and revenue growth • Operate across both: B2C (individual creators, professionals) and B2B (Teams / enterprise product) • Develop growth narrative as the platform expands from images → video → audio • Track and optimize performance across channels with clear attribution

Role Description We are hiring a Founding AI/ML Engineer to architect and ship production-grade AI systems transforming one of the world’s largest and least automated industries — construction. This is a product-first AI engineering role focused on multimodal intelligence, document understanding, and agentic workflow automation. You will work directly with the Chief AI Officer and founders to move from low-automation to high-automation mode, where AI agents perform core operational work with light human oversight. Qualifications - 6+ years of engineering experience - Experience at an early-stage, high-growth startup - Shipped production-grade AI systems to real customers - Hands-on LLM and/or computer vision experience - Retrieval systems experience (embeddings, chunking, grounding) - Agentic workflow experience (tools, APIs, state management, retries, guardrails) - Strong backend fundamentals: - APIs - Async jobs / queues - Databases - Cloud deployment - Monitoring - Full end-to-end ownership mindset Requirements - Build and ship production-grade AI systems used daily by real customers - Design multimodal pipelines for drawings, specs, and construction documents - Build agentic workflows automating preconstruction tasks: - Document intake - Extraction - Takeoff support - Bid preparation - QA workflows - Build and improve: - Retrieval systems (embeddings, reranking, chunking) - Evaluation systems - Orchestration layers - Optimize model quality, latency, reliability, and cost efficiency - Prototype and iterate quickly on customer-facing AI features - Own systems end-to-end (experiment → deploy → monitor → improve) - Help shape engineering culture and long-term technical direction Benefits - Meaningful early-employee ESOP grant - Employer-funded health insurance (Patch model) - Team / meal stipend - Growth path to technical leadership (optional management track)

• speak with founders, hiring managers, and operators • qualify client hiring needs • run intake calls • present our AI-powered recruiting model • follow up and close pilot clients • coordinate with sourcing and recruiting delivery team • help manage candidate/client communication • support placements when needed

• Define DevOps strategy per project • Set up infrastructure from scratch when needed • Take over long-running projects and scale new scopes • Communicate directly with clients (including CTO-level stakeholders) • Own documentation, processes, and access management • Independently manage time across multiple environments

• Design, build, and maintain evaluation pipelines for production AI agent systems • Instrument multi-agent workflows with tracing and observability tooling • Build evaluation datasets using real production traffic and interaction logs • Develop quality scoring and robustness scoring systems for LLM outputs • Improve reliability of AI systems handling non-deterministic model behavior • Implement and optimize HITL (Human-in-the-Loop) escalation workflows • Analyze production failures and drive architectural improvements • Own the full feedback loop between evaluations, prompt optimization, architecture updates, and re-testing • Contribute to prompt engineering and model optimization strategies • Collaborate on multi-agent orchestration and workflow reliability decisions • Work across backend systems, deployment pipelines, monitoring, and operational sustainment • Participate in production support and on-call responsibilities • Maintain high engineering standards around scalability, observability, and maintainability • Operate independently across development, testing, deployment, and production ownership

• Architect and deploy LLM and agentic AI systems in production • Lead ML solution design across customer engagements • Mentor engineers and influence technical direction • Ensure scalable, maintainable, enterprise-grade ML systems

• Define end-to-end data architecture • Lead cloud-based data platform implementations • Optimize ETL/ELT workflows • Enforce governance and security standards • Mentor engineers • Act as a client-facing technical authority

Role Description We are hiring a Senior Machine Learning Engineer (LLM Engineer) to lead the design, implementation, and productionization of advanced ML and LLM-powered systems. This is not a research-only role. This is a client-facing, production-focused leadership role. - Architect and deploy LLM and agentic AI systems in production - Lead ML solution design across customer engagements - Mentor engineers and influence technical direction - Ensure scalable, maintainable, enterprise-grade ML systems Qualifications - 7+ years of ML / AI / Data Science experience - 5+ years AWS hands-on experience - Deep expertise in machine learning and advanced analytics - Strong experience with NLP and LLM technologies - Mastery of Python and strong SQL proficiency - Proven leadership in customer engagements - Experience shipping ML systems to production Requirements - Experience building agentic systems - MLOps pipeline ownership - Enterprise consulting experience - Experience across multiple industries - Exposure to vector databases and RAG systems Company Description Client delivers enterprise-grade Data and AI consulting solutions for mid-market and enterprise clients across multiple industries. The team partners with globally recognized clients to design and deploy production-grade AI systems with measurable business impact.

• Instrument agents with trace-level observability (Langfuse or equivalent) • Build evaluation datasets from production traffic • Design and operate scoring pipelines (quality + robustness scoring) • Stand up and operate Braintrust or equivalent evaluation framework • Build feedback loops from eval → prompt → architecture → re-evaluation • Implement DSPy / DPO optimization as system matures • Partner with technical leadership on reliability-sensitive architecture decisions • Own dev → test → deploy → on-call sustain for your workstream

• Lead day-to-day engineering team activities including sprint planning, grooming, scrums, and retrospectives • Provide architectural and technical direction across APIs, services, user-facing applications, and core systems • Design, build, and deploy secure and scalable backend services • Introduce and maintain best-in-class agile software development practices • Align engineering roadmaps with business objectives and product release schedules • Manage full team lifecycle: hiring, mentoring, performance management, and leadership development • Act as a key liaison between engineering and cross-functional stakeholders • Oversee technical debt management and ensure sustainable development velocity • Contribute to broader engineering leadership initiatives