Distributed Cloud | Presales Engineer (Cloud, AI & Data)
Compensation
Not disclosed
Location
Lisboa, Lisboa, pt
Work setup
Hybrid
Seniority
Not specified
Employment
Not specified
Experience
Not specified
Category
Solutions engineering
Posted
Skills
agentic-ai
ai
anomaly-detection
aws
azure
bigquery
claude-code
cloud
cloud-native
cloudformation
computer-vision
containerization
data-mesh
data-modeling
data-pipelines
data-vault
docker
embeddings
etl
event-driven-architecture
fine-tuning
finops
gcp
generative-ai
iam
infrastructure-as-code
jira
kafka
kubernetes
llm
machine-learning
microservices
nlp
olap
openai
pre-sales
prompt-engineering
proof-of-concept
python
sagemaker
sales-enablement
serverless
sketch
sql
terraform
transformers
vault
vector-search
vertex-ai
Nice to have
At least 6 months of hands-on experience using AI code assistance accelerators like Gemini Code Assist, Claude Code, Cursor, Windsurf, or similar tools, and you're comfortable working with coding-focu
6+ years building real AI or data projects, with some architecture responsibility
Master's or PhD in a relevant field (but this won't make up for lack of practical experience)
Infrastructure-as-code (Terraform, CloudFormation) and cloud governance (FinOps, IAM)
Docker and Kubernetes—you know containerization is more than "it works on my machine"
You've presented to C-suite or board-level stakeholders without flinching
You've actually failed at something ambitious and learned from it
Full description
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.
Distributed Cloud | Presales Engineer (Cloud, AI & Data) at Devoteam (Lisboa, Lisboa, pt) — aijobsearch
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.
The Non-Negotiables:
You can build PoCs, not just slides – You're able to spin up small applications and proofs-of-concept that actually run and prove value to the customer, whether you're writing the code yourself or orchestrating AI code assistants to do the heavy lifting
Cloud is your playground – You're comfortable with at least one of GCP, AWS, or Azure, and you know how to stitch managed services together into something that works reliably in the real world
You speak Data, AI & Modern Apps fluently – You should have real experience with at least 2-3 of these:
Machine Learning – You understand supervised/unsupervised learning, have worked with real models, and know when ML is overkill
Natural Language Processing – Transformers, embeddings, RAG, prompt engineering—you've actually used them in projects, not just in blog posts
Computer Vision – Image classification, object detection, you know the fundamentals
Generative AI – You've experimented with LLMs, fine-tuning, multi-agent systems, vector search, and you know the difference between a cool demo and something that can survive production
Application Modernization – You've seen legacy apps up close, helped refactor or replatform them, and understand patterns like strangler fig, decomposing monoliths, and moving toward microservices or modular architectures
Cloud-Native Migrations – You've been involved in moving workloads to cloud-native architectures (containers, serverless, managed databases) and understand the trade-offs and migration paths
Data Streaming – Kafka or similar, event-driven systems, real-time processing—you know why and when streaming matters
Data Modelling – You've worked with Kimball, Data Vault, or modern approaches, and you know when to denormalize and when not to
Databases are not a mystery – You understand the basics of OLTP vs OLAP, have worked with at least a couple of real database engines, and can reason about performance and fit at a high level
You get how customers buy tech – You understand that PoCs, pilots, and architecture reviews are part of a buying journey, and you know how to keep both the CTO and the engineering team on board.
Bachelor's degree in Computer Science, AI, Data, or related field (or equivalent demonstrable experience that makes us ignore the degree requirement)
Customer presence: Sometimes we need to be in person with customers to present our cool ideas and solutions. And it’s great to have direct feedback at the table, so it’s expected that you’re willing to do some light travelling in Portugal, mainly around the offices areas (Lisboa, Porto, Aveiro)
Nice-to-Haves:
At least 6 months of hands-on experience using AI code assistance accelerators like Gemini Code Assist, Claude Code, Cursor, Windsurf, or similar tools, and you're comfortable working with coding-focused LLMs such as Gemini, Claude Sonnet/Opus, or OpenAI-style code models to accelerate PoCs and solution experiments
6+ years building real AI or data projects, with some architecture responsibility
Master's or PhD in a relevant field (but this won't make up for lack of practical experience)
Infrastructure-as-code (Terraform, CloudFormation) and cloud governance (FinOps, IAM)
Docker and Kubernetes—you know containerization is more than "it works on my machine"
You've presented to C-suite or board-level stakeholders without flinching
You've actually failed at something ambitious and learned from it
What Devoteam Portugal Offers
Work on genuinely interesting problems – Not cookie-cutter consulting; we handle complex, end-to-end transformations in data, AI, and cloud
Room to grow – Directors, architects, specialists—we promote from within when you're ready
Portugal vibes – We're based in Portugal and proud of it: great weather, great food, and a growing tech scene. We work flexibly and remotely, but we genuinely like to meet up in person—office days, team days, meetups, and the occasional after-work drink are part of how we stay connected as a team
Tools & learning budget – We invest in your growth. Take courses, attend conferences, experiment with new tech
Collaboration over hierarchy – We're lean and mean. You'll talk directly to decision-makers, not through three layers of management
Flexibility – Remote work, flexible hours, and we actually trust you to get the job done
Performance-related Compensation - part of your compensation is directly tied to your performance, with conversion ratios and influenced revenue playing an important part. The better you are, the more you make!
The Devoteam Group works for equal opportunities, promoting its employees based on merit and actively fights against all forms of discrimination. We are convinced that diversity contributes to the creativity, dynamism and excellence of our organization. All of our vacancies are open to people with disabilities.