Ever want to build mind-bending solutions that actually solve real problems? We’re looking for a Presales Engineer who can translate “our data is a mess” into “here’s how we’ll fix it with some cloud magic and AI sprinkled on top.”
You’ll be the person who can fluently speak both nerdy tech and business reality without needing a translator. You’ll design architectures, demo them to customers who get excited (or look confused—your job to bridge that gap), and then work with our delivery team to actually make it happen. Think of yourself as the bridge between “what’s theoretically cool” and “what actually works in production.”
And you’ll use AI tools proficiently and wisely to build high-quality artifacts and proposals. We are AI-driven at our core, and we make no apologies for it.
What You’ll Actually Do
Design and demo killer architectures – Take customer pain points and sketch out solutions using GCP, AWS, Azure, or hybrid setups that make your delivery team nod in approval
Deep dive into their tech stack – Understand their data pipelines, AI needs, cloud strategy, and database nightmares (yes, there will be vendor rants)
Run technical workshops – Explain why vector search is cool, how multi-agent systems work, and why they shouldn’t just throw all their data into an LLM
Collaborate with Sales – Help identify what the customer really needs (hint: it’s usually not what they ask for initially)
Prove it works – Design and contribute hands-on to proofs-of-concept that actually validate feasibility, not just pretty PowerPoint slides
Bridge the gap – Keep delivery teams in the loop early so there are no nasty surprises when implementation starts
Keep things real – Balance technical ambition with what’s actually achievable given budget, time, and the customer’s existing tech debt
A Day in the Life of a Presales Engineer at Devoteam Portugal
Your day won’t be anything like this, to be honest, but you can get an idea of the activities our Presales engineers typically go through in their day-to-day work. Some are more frequent than others.
08:45 – Morning Coffee & Work organization with the team
You roll in (remotely or at our office) and catch up with the team. There's a question about whether Vertex AI or SageMaker is better for a customer use case. You jump into the thread with nuance—it depends. You discuss and share the pain of having too many engagements at once. This is, sometimes, a very fast-paced job.
10:00 – Deep Dive Call with the Customer
You’re on a call with a financial services customer drowning in batch data processing. Their current ETL is held together by SQL prayers and scheduled tasks. You ask probing questions: What’s your data volume? Latency requirements? Budget for cloud migration? You’re taking mental notes on architecture patterns that might fit.
11:30 – Whiteboard Time (Virtual or Physical)
Back at the office (or in draw.io), you’re sketching out a solution architecture. They need real-time data streaming for risk analysis, so you’re designing a data mesh approach with Solace, BigQuery, and maybe some agentic AI for anomaly detection. You snap a photo of the whiteboard and send it to the team chat.
12:30 – Lunch + Async Context Dump
You document your thoughts on the opportunity in Jira and a draft slide presentation. You involve the area lead: “Hey, we have this situation and, from what we discussed before, I think this is a possible solution for it, but I’m worried about the total effort vs the customer benefit. Any thoughts on your side?”
14:00 – Sales Enablement Sprint
The sales team has a prospect call at 15:30. They want you to join and handle the technical architecture discussion. You prep a simple (but not too simple) slide deck showing cloud options without overcomplicating it.
15:30 – Prospect Call
You present three architecture patterns for their AI-powered recommendation engine. One is cloud-native (cool but pricey), one is hybrid (balanced), one is mostly on-prem with cloud augmentation (safe, boring). The prospect gets genuinely interested in option 2. You book a follow-up technical workshop.
16:45 – PoC Validation Check
You’re overseeing/building a proof-of-concept for another customer (Python, SQL, vector embeddings, the works…). You review the technical approach with the delivery team and get the approval for the next phase.
17:15 – Proposal Dry-run and Validation
45-minute team meeting presenting and improving the proposals that need to be delivered in the next couple of days. A bit like “Shark Tank” with your team mates, to get the proposal. Occasionally, you discuss specific technical challenges about the problems and solutions proposed coming from last week’s customer call: “Turns out they can’t shard their database. We need a Plan B.”
17:45 – Catch-Up & Tomorrow's Prep
You wrap up email, prep for tomorrow’s customer demo, review some new AI/ML research that might be relevant, and update your solution library with that neat architecture pattern you learned from the financial services call.
