Top Tech Jobs & Startup Jobs

Leads enterprise AI adoption programs from assessment through workshops, hands-on enablement, adoption measurement, and team self-sufficiency. Builds client relationships, develops internal AI champions, creates reusable prompts and templates, ensures compliance with data-governance and responsible-AI policies, reports business impact and ROI, and identifies expansion opportunities. The role requires practical AI fluency, strong facilitation, change-management expertise, customer success capabilities, and executive communication skills.

Build unstructured-data ingestion and context-layer pipelines for an investment group. Responsibilities include historical backfills, document parsing, entity resolution, deduplication, incremental API synchronization, embeddings, vector and graph storage, access scoping, lineage, PII protection, orchestration, monitoring, cost control, and operational runbooks.

Client-facing AI Analyst responsible for eliciting executive strategy, OKRs, investment policies, and operating processes; defining AI agent skills and evaluations; interpreting process-mining data; modeling ROI; prototyping LLM solutions; driving executive adoption; and documenting requirements, decisions, and runbooks. The role partners with executives, an AI Architect, and a Data Engineer to deliver an AI context layer and executive pilot.

Owns the company’s operational, financial, and execution systems so the CEO can focus on vision and growth. Responsibilities include managing executives and operations, enforcing OKRs and KPIs, overseeing the company operating system, allocating resources, managing budgets and forecasting, improving profitability and risk visibility, and managing industry partnerships. The role requires prior senior strategy consulting leadership, hands-on operating experience, P&L or budget accountability, operating-system implementation, and experience leading distributed teams.

Own the architecture and production delivery of a real-time voice AI copilot. Lead streaming speech-to-text, LLM extraction, Chrome extension integration, AWS infrastructure, latency optimization, evaluation, observability, cost control, concurrency, data isolation, monitoring, and rollback. Lead the AI engineering pod, manage client stakeholders and scope, present evidence-based results, deliver production epics, and transfer knowledge to client and internal teams.

Lead end-to-end development of an AI mahjong companion, including the gameplay decision model, LLM explanation layer, valid-action integration, win detection, training pipeline, evaluation harness, and low-latency serving. Own architecture, AWS deployment, Langfuse observability, and the two-second response budget while leading a small AI pod and communicating technical decisions to the client’s CTO and engineering team.

Lead creation and commercialization of packaged BFSI AI/Cloud solutions: prioritize use cases (KYC/AML, Fraud, Document AI, RM copilot, RegTech), define value props/pricing/playbooks, build reference architectures (LLM/RAG, data governance, security), drive pre-sales/PoV to production, establish hyperscaler/ISV alliances, and produce thought leadership to accelerate deals and scale offerings.

Lead enterprise sales for AI solutions in financial services: generate pipeline, own full sales cycle using MEDDIC, hit quota, build C-suite relationships, coordinate scoping with AI engineering, manage proposals/RFPs, contracts, and CRM while leveraging AI tools to scale outreach.

Lead engineering enablement across eight companies to cascade CTO AI vision into team discipline. Detect and package successful practices, run cross-company diffusion, operate cadence of validation and demos, measure adoption and outcomes, triage deeper needs to PoCs/workshops, and capture reusable artifacts and metrics.

Identify and validate high-value AI use cases for financial services, lead discovery workshops, build ROI-driven business cases, map and redesign workflows, define technical requirements and acceptance criteria, prototype solutions with no-code tools, implement AI governance and ontologies, and ensure business-aligned delivery and compliance.