Join Akvelon — build products used by millions!
Akvelon is an IT company with 20+ years of experience and 1,200+ engineers across 15+ locations worldwide.
We work with both well-known global tech companies, including Microsoft, Facebook, Airbnb, Dropbox, and Pinterest, and with growing startups.
Our teams are involved in different types of engineering projects, from cloud solutions and AI/ML systems to big data, web, and mobile applications.
Since we are remote-first, our engineers work in distributed teams with flexible hours. We value ownership, clear communication, and the ability to take responsibility for your part of the work.
About the role
Enterprise clients come to us with one of two requests: too broad —
"we need AI" — or too narrow —
"let's add a chatbot." Engineering can then build something technically sound that solves nothing that matters.
This role closes that gap. You embed in the client's context, work out how their process actually runs, decide where AI genuinely creates value (and where it doesn't), and stay with the solution through POC, production, adoption, and measured outcome. The working model is close to a Forward Deployed Engineer's — deep client involvement, a fast path from ambiguity to something working, accountability for the result. Your primary tools are discovery, stakeholder alignment, value framing, and delivery judgment rather than code.
You'll work on a few strategic engagements rather than a wide portfolio of shallow accounts. You are accountable for outcomes in production, not for the number of POCs.
What you'll own
- Work directly with enterprise clients to understand and map business processes, pain points, users, data, constraints, bottlenecks, and opportunities for automation.
- Run discovery workshops with business sponsors, domain experts, users, IT, and security — reconstructing the real workflow, its exceptions, economics, and cost of error.
- Build the client's AI opportunity map and prioritize by value, feasibility, data readiness, adoption complexity, risk, and time-to-first-result.
- Determine whether AI is actually the right solution, and make informed decisions between deterministic automation, classical ML, GenAI, RAG, and agentic workflows — including the case where the process and the data need fixing first.
- Define the MVP, non-goals, acceptance criteria, baseline, KPIs, and a pre-agreed scale / revise / stop gate — then hold that gate. No permanent pilots.
- Lead AI/GenAI initiatives from discovery and POC through production rollout, adoption, and outcome measurement, coordinating engineers, architects, and delivery teams.
- Design for real adoption: workflow integration, human-in-the-loop controls, champions, onboarding — and diagnose adoption when it stalls.
- Prove the value honestly. Set the baseline before the pilot, separate adoption from operational and business impact, be explicit about the limits of attribution, and never present an estimate or self-reported saving as a validated outcome.
- Turn what works into reusable patterns, accelerators, and playbooks that feed product and delivery.
Roughly how the time splits: 30% client discovery and alignment · 25% product shaping and value case · 20% delivery orchestration · 15% adoption and measurement · 10% productization.
What we're looking for
- 7+ years of combined experience across product management, consulting, solution delivery, digital transformation, or customer-facing technology roles.
- 3+ years of end-to-end ownership — several real cases where you personally took an ambiguous business problem through implementation and measured the outcome. We'll ask about them in detail.
- Hands-on experience with AI/ML, GenAI, or data products — beyond using AI tools for your own productivity.
- Comfortable with enterprise stakeholders and competing interests, from executive sponsor to process owner to security.
- Able to run discovery at workflow and unit-economics level, then discuss APIs, data flows, integrations, evals, security, and production constraints with engineers.
- Experience defining baselines, KPIs, and go / no-go criteria for pilots.
- Readiness to run onsite workshops with clients and work across US time zones.
- Client-ready professional English.
Technically: you don't need to be an engineer. You do need to explain what LLMs can and can't do without marketing promises, tell a prompt-only prototype from RAG, tool use, agentic workflows, or fine-tuning, know why evals, fallbacks, human approval, and security boundaries exist — and tell demo quality from production readiness.
Nice to have
- Production launches of LLM / RAG / agentic solutions.
- Background in AI consulting, AI product management, deployment strategy, solution architecture, digital transformation, or enterprise SaaS implementation.
- Experience in IT services or consulting, where client value, delivery feasibility, and the commercial context have to be balanced at the same time.
- Experience taking a POC into a rollout, an expansion, or a repeatable offering.
- Deep expertise in one domain: financial services, healthcare, retail, manufacturing, telecom, logistics, or enterprise operations.
- Experience with change management and adoption programs.
- Experience working with security, privacy, legal, or compliance stakeholders on enterprise AI solutions.
Base compensation