Jobs at Replicant

Prospect and close new enterprise opportunities, own the sales pipeline from lead generation through forecasting, and collaborate with SDRs to develop qualified leads. Educate enterprise prospects on AI technology, translate complex solutions into customer benefits, and help build Replicant’s sales playbook and culture. The role focuses on selling AI-powered customer service solutions to businesses with more than $1 billion in revenue.

Leads corporate FP&A, annual planning, forecasting, long-range modeling, board and investor reporting, fundraising analysis, capital strategy, IPO readiness, and scenario planning. Partners with Accounting on metrics and public-company reporting standards, upgrades planning systems, and builds the corporate finance team. Requires extensive finance leadership, SaaS metrics expertise, advanced financial modeling, and hands-on IPO or public-company experience.

Own post-launch bugs, production issues, and enhancements for AI agents and related products. Investigate root causes across logs, code, conversation data, APIs, authentication, telephony, and integrations; implement and test fixes; support customers directly; guide frontline Support Engineers; and build internal tools that improve investigation and resolution speed.

Lead exploration and application of large language models and generative AI, translate research into deployable ML systems, and guide strategic direction. Develop solutions across experimentation, implementation, and deployment while collaborating with engineering and product teams to optimize performance and customer outcomes in contact center automation.

Build and improve Replicant’s AI-native platform, including site reliability, CI/CD, developer tooling, observability, incident management, cloud infrastructure, and autonomous-agent harnesses. Own production infrastructure reliability at scale, reduce operational toil, improve deployment workflows, participate in on-call rotation, and shape platform engineering patterns. The role uses TypeScript/Node.js, Python, Terraform, Kubernetes, Helm, GCP, and modern monitoring tools.

Build Replicant’s internal AI enablement program by developing reusable agent workflows, MCP integrations, agent identity management, enterprise SaaS connections, and cost visibility mechanisms. Partner with Sales, Delivery, Engineering, Finance, People, and other teams to identify opportunities, implement secure AI solutions, create shared templates and skills, build AI literacy, and develop internal champions. Optimize AI infrastructure through prompt caching, model routing, tool consolidation, and improved agent design.

Support pre-sales by owning technical engagements: gather requirements, design AI agent integrations, run hands-on trials, craft prompts, write/review APIs, analyze conversational data, and collaborate with sales, product, and engineering to demonstrate platform value.

Lead the quality engineering efforts for Replicant's AI platform by defining standards, improving testing strategies, and mentoring a QA team to ensure high-quality outputs across software systems.

Build and own production AI agents for assigned customers: discover workflows, design prompts and conversation flows, integrate with customer APIs/CRMs, create test sets, measure and harden agents, monitor live conversations, debug and ship fixes, and feed product improvements. Work customer-embedded to deliver reliable, secure, scalable LLM-powered voice and conversational AI solutions.

Design and deliver full-stack features for AI voice and chat products at enterprise scale. Integrate ASR/TTS and LLM improvements, optimize production systems for latency and cost, participate in on-call rotations, and collaborate with product and ML teams to ship high-impact, scalable solutions.

Lead a 8–15 engineer product squad as a servant leader: hire and grow engineers, own team budget and headcount, drive predictable high-quality delivery, embed AI practices, shape processes and outcomes, ensure engineering excellence (testing, validation, developer experience), and partner across Product and Delivery. Act as a peer in the engineering-management leadership team.