DevOps Engineer (AWS)

Our plans in Lithuania

We’re launching a new R&D team in Vilnius, Lithuania. Two of our team members are already based in Lithuania: a Lead Engineer and a QA Engineer. In Q1, we plan to hire 3 additional engineers, bringing the team to a total of five. By the end of the year, we expect the team to grow to around 10 people, depending on business needs and progress. The team will have a dedicated office in Vilnius, with an emphasis on building a strong local culture through collaboration and team activities. The Vilnius team will work closely with the wider engineering organization, with opportunities for on-site visits and team exchanges at our Amsterdam headquarters and Barcelona hub.

Your team:

You’ll join our DevOps team, working directly with our CTO, Head of AI and alongside a junior DevOps teammate. You’ll collaborate closely with the broader engineering org, supporting the core product engineering team (setting up in Lithuania) as well as our remote AI engineering team on reliability, monitoring, and AWS operations.

This role has a high degree of autonomy and trust: you’ll help set priorities, improve operational standards, and drive cross-team initiatives that make deployments safer, incidents easier to manage, and production more observable and cost-efficient. You’ll be the point person who helps connect the dots across teams when DevOps issues arise, while keeping stakeholders aligned through clear updates and lightweight operational processes.

Why this role exists

As LearnWise scales, we want to level up how we run production on AWS: clear cost ownership, strong observability, and fast, confident operations.

In this role, you’ll take the lead on AWS-focused DevOps and observability—building cost monitoring and reporting across services, creating latency/performance dashboards the team uses every day, improving alerting so it’s high-signal, and bringing error monitoring (including Sentry) into one reliable workflow. You’ll work closely with engineers (especially our jr. DevOps Tomas) to remove operational friction, speed up debugging, and make production more predictable as traffic and usage grow.

What you’ll do:

• Own production performance and cost across AWS and MongoDB Atlas, with clear visibility into where spend and latency come from—and a steady cadence of improvements.
• Drive AWS optimization (FinOps + reliability): identify high-impact opportunities such as right-sizing, storage/network tuning, and ongoing cost/performance reviews.
• Reduce AWS spend without degrading reliability or developer velocity, balancing cost with operational safety and shipping speed.
• Benchmark and evaluate architecture changes and make rollout plans based on evidence—including potential changes like moving vector workloads to S3 Vectors (or equivalent AWS-native vector approaches).
• Improve MongoDB Atlas performance end-to-end: audit and refine schemas, indexes, query patterns, aggregation pipelines, and connection usage.
• Reduce p95/p99 latency and eliminate common scaling pitfalls, including index bloat, write amplification, inefficient query shapes, and hot partitions.
• Establish a practical capacity and scaling approach as usage grows, including clear criteria for when/if sharding is warranted.
• Build dashboards and alerts that make regressions obvious, covering cost, latency, saturation, slow queries, and error rates—so issues are caught early and triaged quickly.
• Create lightweight guardrails so performance doesn’t rely on heroics, such as sensible defaults, automation, thresholds, and operational checklists.
• Jump in on startup-style ad-hoc work: performance investigations, production incidents, and architecture reviews as needed.
• Unblock teams by turning fuzzy performance problems into clear plans and shipped fixes, partnering closely with engineering and DevOps stakeholders.

Non-negotiable requirements:

• You are smart, pragmatic, and love solving real production problems.
• You can operate as both owner and executor. Sometimes we need a roadmap and coordination; other times we need you to jump into metrics and fix the thing today.
• Strong AWS experience with a track record of improving cost and performance across compute/storage/networking (not just “deployed to AWS”).
• Strong data modeling skills and experience with handling migrations from the database and codebase perspective
• Python fluency and in particular, familiarity with Motor.
• You’re comfortable going deep: query planner/explain plans, indexing tradeoffs, aggregation performance, replication/read-write concerns, and diagnosing bottlenecks with evidence.
• Deep familiarity with the terminal, your tooling, and git.
• Professional fluency in English (written & spoken).

Important, but with some wiggle room:

• Experience with Mongo Atlas Search evaluating or migrating vector/search architectures (e.g., S3 Vectors, dedicated vector DBs, hybrid approaches).
• Strong monitoring/observability instincts (metrics, dashboards, alerting; ability to define “what good looks like”).
• Prior experience in a fast-moving startup or scale-up environment.

Nice to have:

• Work experience with FastAPI or similar Python async web frameworks.
• “Jack of all trades, master of one” – you’re an expert in DevOps; but you can and enjoy jumping into adjacent problems and doing development work occasionally.

How we work

• Open, asynchronous communication: all communication happens in public channels by default. We have very few meetings and rely on async communication.
• Strong team spirit: we’re a team full of fantastic humans. Everyone at LearnWise loves and cares deeply about what they’re doing. If you’ve only worked at corporate jobs, the experience can be overwhelming 😉
• Small team, big ownership: you’ll own features and systems end-to-end. We don’t hold hands or micromanage. We make sure everyone has the support they need to thrive and deliver their best.
• Quality and speed: “good enough” is often better than perfect. Delivering real value to our customers is often more important than pristine coding practices. We do have refactor-parties sometimes 🙂

Location & schedule

• Full-time role in Lithuania with an opportunity to go to the office.
• Remote is OK.
• Flexible schedule and a “no strict 9–5” mentality, with real respect for work–life balance.

Compensation & benefits

• Competitive salary: €5,000–€7,000 gross.
• Flexible working arrangements, including remote work within Europe.
• An office in Vilnius for those who don’t want a fully remote setup.
• Focus on work–life balance, not clock-watching.
• Opportunity to build cutting-edge AI technology and make a real impact in EdTech.
• Join team meet-ups globally (e.g. our offsites like Costa Brava).
• Comprehensive health insurance package.
• Annual learning & development budget.
• Home office setup stipend.

Hiring process

• Intro chat (15–20 min): quick conversation with our CTO or Head of AI to learn more about you and the role.
  Technical deep dive (30–45 min): systems discussion focused on AWS + MongoDB Atlas optimization and how you approach real performance/cost problems.
• Practical exercise or pairing session (45–60 min): focused on diagnosing bottlenecks, proposing fixes, and explaining tradeoffs.
• Culture/leadership chat (30 min): meet another founder or leader to ensure mutual fit.
• Offer.

Questions we would like to discuss with you:

1. Do you have hands-on experience working with MongoDB in production environments (including Atlas)? It’s nice to have.
2. How many years of experience do you have with AWS cost and performance optimization.
3. How comfortable are you optimizing AWS spend and latency without degrading reliability (right-sizing, storage/network tuning, architectural changes)?
   – Expert – I regularly deep-dive into query planners and metrics to fix bottlenecks.
   – Comfortable – I’ve contributed to optimization efforts.
   – Not comfortable – I have limited exposure.
4. Do you have experience evaluating or migrating data/vector/search workloads across architectures (e.g., Atlas Search, S3 Vectors, vector DBs, hybrid approaches)?