Product Engineer - DoiT Labs(Romania)

DoiT is a global technology company that helps cloud-driven organizations optimize their cloud usage and drive business growth. The Product Engineer will be part of DoiT Labs, focusing on building AI-powered products that enhance cloud cost optimization and improve user experiences through full lifecycle problem-solving.

Responsibilities:

• Full-lifecycle problem solving
• Own problems end-to-end: from understanding user pain, through solution design, implementation, release, measurement, and iteration - not just the coding step
• Engage directly with customers and internal domain experts to build deep empathy for the workflows and challenges of cloud operators and FinOps practitioners
• Translate ambiguous problem spaces into clear, thin-sliced increments that can be shipped, measured, and learned from quickly.AI-first product development
• Use AI tools daily to amplify your own engineering work - coding, analysis, research, and prototyping
• Design and build AI-powered features as a default approach: intelligent recommendations, automated insights, natural-language interfaces, and predictive capabilities for cloud cost optimization
• Make informed decisions on model selection, prompt engineering, latency/accuracy/cost tradeoffs, and responsible AI considerations as a core part of your engineering practice
• Fast iteration and shipping
• Operate with a bias toward action: prototype rapidly, ship frequently, and validate ideas through real customer usage rather than prolonged planning cycles
• Build experiments and MVPs that generate measurable learning - and use those learnings to decide what to invest in next
• Maintain high engineering standards without letting perfection slow down delivery; know when to take deliberate shortcuts and when to invest in durability
• Full-stack engineering across the cloud
• Build across the full stack - backend services, APIs, data pipelines, and frontend interfaces - whatever the problem demands
• Work with cloud-native billing, usage, and operational data from AWS, GCP, and Azure to build cost optimization and governance capabilities
• Develop solutions that operate across Kubernetes environments, data cloud platforms, and broader multi-cloud infrastructure
• Build state-of-the-art solutions for Generative AI observability and FinOps - enabling customers to understand, monitor, and optimize the cost and performance of their AI/ML workloads across cloud environments
• Ownership and customer impact
• Take full ownership of the solutions you ship - including reliability, user experience, and measurable outcomes
• Define what success looks like for your work using clear metrics: adoption, activation, workflow improvement, cost savings delivered, and customer-reported impact
• Participate in customer conversations and feedback loops to continuously validate direction and surface new opportunities

Requirements:

• 5+ years of professional software engineering experience, with demonstrated ability to deliver complete products or features end-to-end
• AI-first mindset - you actively use AI tools to accelerate your work and instinctively look for opportunities to embed AI into what you build
• Strong full-stack engineering capability: you can work effectively across backend, frontend, APIs, and data layers without being confined to a single technology or language
• Solid understanding of public cloud platforms (AWS, GCP, and/or Azure) - including core concepts like compute, networking, IAM, Kubernetes, and billing/cost structures
• Product-oriented thinking: you care as much about why you're building something and whether it works for users as you do about how it's built
• Comfort with ambiguity and fast-changing priorities - you thrive when you need to figure out the right problem to solve, not just the right solution
• Strong customer empathy and communication skills -- you can engage with technical practitioners, synthesize feedback, and explain complex ideas clearly
• Excellent communication skills in English, both written and verbal
• Self-motivated, resourceful, and effective in a remote, fast-moving team environment
• Hands-on experience building AI/ML-powered product features (LLM integration, recommendation systems, intelligent automation)
• Background in a startup, research lab, or '0-to-1' product environment where you wore multiple hats
• Familiarity with data cloud platforms (Snowflake, Databricks, BigQuery) and their cost/usage dynamics