Senior BackEnd Developer

The Gig

OGRE is a AI-based tech company, aiming to empower the energy sector with an innovative energy forecasting and optimization platform. We are already delivering our service to multiple Romanian and international clients aiming to optimize their energy flows, reduce costs for end consumers, minimize waste and reduce carbon emissions.

We are a small team of smart, ambitious engineers and data scientists who share a passion for building great technical solutions. With solid academic backgrounds – including research experience from Oxford, Cambridge and Berkeley – and complementary expertise, we are keen to disrupt the energy sector with a scalable and user-friendly energy forecasting and optimization platform.

If you will ask our colleagues what they value the most about working at OGRE, you would probably hear:

• the opportunity to work on technology with real-world impact for the future.

• a clear and efficient task allocation process.

• a healthy culture of collaboration, learning and solution-oriented thinking.

What we are looking for:

We are growing and looking for an experienced, self-driven BackEnd Developer to join us in building the AI Energy Hub platform. If you are excited by AI, thrive on hands-on work and want to make a tangible difference in the energy sector – let’s talk. Below, you will find more details about this role and what you will have at OGRE.

Minimum requirements:

• Bachelor’s degree in computer science, information systems or related field.

• 4 years of relevant work experience.

What we offer:

• Hybrid work with 1-2 days in the office per month. We have an ultra-modern office in Bucharest, in one of the Globalworth buildings.

• Build a platform that helps global clients optimize their energy flows and reduce carbon emissions.

• Work alongside some of the sharpest minds in AI and energy.

Benefits

• Attractive compensation package

• Flexible working hours

• Permanent employment contract

• High-tech hardware

• Friendly and supportive team

What will you be doing?

• Design and implement reliable, secure, well-documented REST services (FastAPI) with schema-first contracts (OpenAPI), versioning and pagination.

• Model, query, and tune Postgres; write robust SQL (indexes, transactions, migrations); manage time-series workloads with TimescaleDB; handle large files and complex data types.

• Build cloud-native and serverless workloads using managed services, events and queues; design for scalability, cost efficiency and least-privilege IAM.

• Orchestrate workers and task queues (e.g., Celery) for batch/async processing and integrations.

• Automate ML pipelines (feature/metadata flows, batch/stream interfaces, model-serving endpoints) in partnership with the Data Science team.

• Keep pipelines healthy, drive automated checks and ensure fast, safe promotions and rollbacks.

• Provision and evolve infrastructure reproducibly (networking, IAM, data stores, queues, secrets, serverless, containers) using Terraform.

• Instrument logs/metrics/traces; maintain actionable dashboards and alerts (Prometheus/Grafana).

• Apply secure-by-default patterns - TLS/mTLS, secret management, OAuth2/OIDC/JWT, least-privilege IAM and encrypted data handling.

What the ideal candidate should bring:

• Strong modern Python (typing, async where appropriate), packaging/dependency hygiene and performance profiling; or comparable experience in another backend language.

• Expert in relational modeling and Postgres tuning; confident with TimescaleDB (or equivalent time-series concepts).

• Comfortable building stable public/internal APIs; pragmatic about backward compatibility and schema evolution.

• Daily Docker; strong grasp of cloud primitives and managed services; familiarity with Kubernetes orchestration concepts.

• You’ve owned CI/CD and care about shortening feedback loops and eliminating toil.

• Hands-on Terraform experience for production systems.

• You care about operations, error budgets and fixing issues at root cause.

These skills are very nice to have:

• Caching (Redis), queues/streams (SQS/Kafka/Pub/Sub), and object storage patterns.

• Basic feature-flagging and experimentation support.

• Model packaging/serving experience (batch or real-time) and MLOps collaboration.

• Cost awareness and capacity planning.