Jobs at Provectus

Sources and qualifies new business across North America, builds and manages pipelines, conducts outbound prospecting, leads discovery conversations, and partners with leadership to close consulting and technology-services opportunities. The role targets mid-market and enterprise clients, requires independent prospecting and negotiation skills, and involves attending industry events and client meetings with approximately 40% travel.

Owns partner-sourced and self-generated enterprise sales motions for AI consulting engagements. Manages Anthropic and AWS relationships, qualifies leads, runs discovery through signed SOW, develops co-selling opportunities, maintains joint pipelines, and leads partner business reviews. Independently prospects and closes Financial Services and Life Sciences accounts, collaborates with delivery architects on solution scoping, and provides market intelligence to leadership and marketing. Travel of up to 40% is required.

Owns partner-sourced and self-sourced enterprise sales for AI consulting engagements. Manages Anthropic and AWS relationships, qualifies leads, runs discovery through signed statements of work, develops new enterprise accounts in Financial Services and Life Sciences, coordinates solution scoping with delivery and architecture teams, leads co-selling and joint pipeline activities, and provides market intelligence to leadership and marketing.

Sources and qualifies new business opportunities across North America, builds sales pipelines, conducts outbound prospecting, leads discovery conversations, and partners with leadership to close consulting and technology services deals. The role focuses on selling AI and cloud solutions to mid-market and enterprise clients, requires independent prospecting and negotiation skills, and includes attendance at industry events and client meetings with approximately 40% travel.

Translate enterprise AI engineering work into compelling videos, articles, demos, and social content. Build marketing automation tools such as scrapers, agentic case-study generators, microsites, and pipeline dashboards using Python or TypeScript. Own publishing and measurement across major channels, run marketing experiments with real budgets, and connect storytelling to business results. The role requires strong writing, hands-on coding, genuine AI expertise, a portfolio demonstrating range, and willingness to work in New York or San Francisco.

Own full-cycle recruiting for forward-deployed engineering and leadership roles across the US. Source passive AI and technical talent through LinkedIn, GitHub, communities, referrals, and talent mapping. Build Provectus’s talent community through meetups, workshops, hackathons, university programs, and industry events. Partner with hiring managers and senior leadership on hiring criteria, workforce planning, compensation benchmarking, candidate experience, and offer negotiation.

Build and deploy production GenAI systems, agentic workflows, retrieval pipelines, and data services for enterprise customers. Learn operational domains firsthand, redesign business processes, create evaluation harnesses, write production Python or TypeScript, and deliver observable AWS-based systems. Partner with executives, engineers, and operators to drive adoption and measurable business outcomes while contributing reusable industry blueprints.

Own two revenue motions: convert Anthropic and AWS partner-sourced enterprise leads into consulting clients and independently build pipeline in Financial Services and Life Sciences. Manage relationships with partner and field sales teams, lead co-selling and joint go-to-market activities, run discovery through signed SOW, prospect enterprise decision-makers, scope consulting proposals with delivery teams, and provide market intelligence to leadership. Travel up to 40% for customer and partner engagements.

Manage an East Coast portfolio of Provectus clients to drive renewals and expansion revenue. Track engagement health with delivery leads, manage renewals/SOWs, identify upsell and cross-sell opportunities, run executive business reviews, maintain CRM and revenue forecasts, and hit quarterly expansion/renewal targets. Collaborate with BDMs for handoffs and travel to customer meetings and workshops (up to 40%).

Design and implement cloud-native AI solutions, build relationships with clients, and optimize AI systems. Mentor others and lead architecture decisions.

Embed with customers to learn their workflows, design and ship production GenAI/LLM solutions on AWS, build eval suites, operate and iterate systems, and partner with senior stakeholders to drive measurable business outcomes.

Embed with customers to learn their workflows, redesign and build production GenAI/LLM systems on AWS, ship and operate code, evaluate non-deterministic models, and own business outcomes.

Build and deploy production GenAI systems, including LLM applications, agentic workflows, retrieval pipelines, structured extraction, and evaluation harnesses. Embed with client teams to learn operational domains, redesign workflows, write production code, and drive adoption. Own delivery outcomes and business KPIs while working across backend services, data pipelines, AI systems, and AWS cloud infrastructure. Collaborate with operators, engineers, executives, and Forward Deployed Executives in ambiguous, customer-facing engagements.

Designs and deploys production-grade generative AI, RAG, and agentic systems for enterprise clients. The role combines client workflow discovery, architecture, production coding, cloud-native AWS delivery, evaluation and monitoring, cost optimization, technical scoping, stakeholder communication, and knowledge transfer. Candidates must independently solve ambiguous problems, engage executives and engineers, and own technical outcomes across industries including financial services, insurance, healthcare, and life sciences.

Build and deploy production-grade generative AI and agentic systems for enterprise clients. Responsibilities include developing RAG applications, evaluation harnesses, backend services, data pipelines, and cloud-native AWS deployments; implementing LLMOps and AgentOps practices; monitoring quality, drift, cost, and latency; integrating APIs; improving reliability; contributing to architecture and reusable blueprints; and mentoring AI engineers in a forward-deployed consulting environment.

Build and deploy production-grade generative AI and agentic systems for enterprise customers. Responsibilities include developing RAG applications, backend services, data pipelines, evaluation harnesses, monitoring, guardrails, and cloud-native AWS infrastructure. The role owns model and agent reliability, cost, latency, drift detection, and release quality. It also involves architectural decisions, customer handover, documentation, mentoring engineers, and collaboration with forward-deployed executives and engineers.

Build and deploy production generative AI systems, including LLM applications, agentic workflows, retrieval pipelines, and evaluation harnesses. Work directly inside customer operations to understand domains, redesign business processes, and ship maintainable cloud-native solutions on AWS. Own adoption and measurable business outcomes while collaborating with executives, operators, and engineering teams. Contribute to reusable industry blueprints and remain hands-on across backend, data, and AI development.

Build and deploy production generative AI, RAG, and agentic systems for enterprise clients. Translate business workflows into technical solutions, develop evaluation harnesses, write production code, and deliver cloud-native systems on AWS. Lead architecture reviews, technical proposals, scoping, monitoring, cost optimization, and client handoff. Partner with senior stakeholders, own technical direction, mentor engineers, and help evolve reusable AI Blueprints across financial services, insurance, healthcare, and life sciences.

Design and deliver cloud-native AI and LLM-based solutions from discovery through production. Lead presales, architecture, RAG and agentic implementations, deploy and operate ML/LLM systems, write production-grade Python, build ETL/ELT pipelines, implement MLOps/LLMOps/AgentOps, mentor engineers, and manage client technical relationships.

Designs, builds, and deploys production-grade Python AI systems, including RAG and agentic workflows, for enterprise clients. Responsibilities include architecture ownership, backend and API development, AWS deployment, system design decisions, client communication, presales support, technical documentation, mentoring, code reviews, testing, and production AI/ML model maintenance. The role requires embedded client delivery, direct stakeholder engagement, and end-to-end ownership from discovery through implementation.