Peakflo
Jobs at Peakflo
Let Your Resume Do The Work
Upload your resume to be matched with jobs you're a great fit for.
Success! We'll use this to further personalize your experience.
Recently posted jobs
Financial Services
Serve as the primary client advocate for B2B SaaS customers: manage implementations, document AS-IS/TO-BE designs, run SIT and UAT, troubleshoot post-hypercare issues, ensure ROI delivery, and drive renewals and long-term relationships.
Financial Services
Investigate escalated production incidents (DB anomalies, failed payments, data migrations), write post-mortems and runbooks, and build AI-powered automation (LLM diagnostics, agents) plus developer tooling. Maintain CI/CD, GCP infrastructure, container deployments, observability, and partner on ERP and payment provider integrations.
Financial Services
Outbound, mid-market SDR internship focused on prospecting and cold-calling to generate qualified meetings/opportunities for Account Executives. Work North American shift remotely from India, collaborate to build sales processes, and support pipeline generation with resilience and professionalism.
Financial Services
Develop and refine voice-optimized prompts and agentic LLM workflows for finance use cases. Build RAG grounding, integrate LLMs with voice/telephony (LiveKit), implement feedback loops, A/B testing, fine-tune models, and collaborate on production ML services and OCR/chatbot components.
Financial Services
Analyze operational data and processes, identify bottlenecks, design scalable solutions, automate workflows and reporting with Python and SQL, build dashboards and monitoring, perform root-cause analysis, document logic and collaborate with product, engineering, and ops teams to improve automation and data quality.
Financial Services
Build and deploy agentic finance AI solutions: design voice‑optimized prompts and conversational flows, integrate LLMs (Gemini/GPT/Claude) with RAG and enterprise data, implement voice/speech/telephony integrations, automate workflows and reporting using Python and SQL, and collaborate across product, engineering, and operations to tune and monitor production ML systems.



