About The Role
We're hiring Backend Engineers at the SDE-2 and SDE-3 level to help design and build the platforms at the core of Astranova's business. You'll work across cloud-native, industry-standard technologies — Kubernetes, gRPC, InfluxDB, Airflow, Pub/Sub and modern CI/CD — solving real problems in IoT data processing, leasing operations, and fleet-scale system design. We're looking for engineers who are strong on fundamentals and equally comfortable using modern AI-assisted workflows to build faster and better.

Key Responsibilities
/ What You’ll Do:
• Design & Development — Architect and build scalable, high-performance backend systems that solve real-world problems in EV leasing and fleet operations.
• System Architecture — Translate business requirements into technical designs; evaluate trade-offs and lead engineering efforts for large-scale, distributed systems.
• IoT & Data at Scale — Build pipelines and services that ingest, process, and serve massive volumes of IoT and telemetry data using time-series databases and modern data engineering tools.
• AI-Native Engineering — Use AI coding assistants and agentic tools as part of your everyday workflow, and build AI/ML-driven features directly into the product — from anomaly detection on IoT data to LLM-powered internal tools.
• Engineering Excellence — Champion best practices — rigorous code review, clear documentation, modular design, and strong test coverage — across the team.
• Mentorship — Guide and grow junior engineers, and help raise the technical bar across the org.
• Cross-functional Collaboration — Partner closely with product, business, and design stakeholders across the full SDLC, from requirements to production. Experience &

Qualifications:
● Experience — 3–7 years building complex, large-scale backend or data engineering systems. ● Core Skills — Strong proficiency in Python (preferred) or Java,



a solid grasp of both low-level and high-level system design. ● Tech Stack — Hands-on experience with microservices architecture, RDBMS (e.g., PostgreSQL), NoSQL (e.g., MongoDB), and messaging systems (e.g., Kafka, Pub/Sub). ● Infrastructure — Practical experience with Docker, Kubernetes, and a major cloud platform (GCP or AWS). ● Fundamentals — Strong grounding in data structures, algorithms, and system-design patterns. ● Education — B.
E. / B.
Tech or equivalent from a reputed institute. AI Skills We Value We're building an engineering culture where AI tools are part of the craft, not a bolt-on. The following are a solid plus — and if you're new to some of them, we'll help you get there.
• Agentic coding tools — Hands-on use of tools like Claude Code, GitHub Copilot, Cursor, or Windsurf for day-to-day development, refactoring, and debugging.
• LLM application development — Experience integrating LLM APIs (Anthropic, OpenAI, or similar) into backend services — function/tool calling, structured outputs, and streaming responses.
• Prompt & context engineering — Comfort designing prompts, system instructions, and context windows for reliable, production-grade AI behavior.
• RAG & retrieval systems — Experience with retrieval-augmented generation, embeddings, and vector databases (e.g., pgvector, Pinecone, Weaviate).
• MCP / tool-use protocols — Familiarity with the Model Context Protocol (MCP) or similar standards for connecting LLMs to external tools and data sources.
• AI workflow automation — Experience using AI agents or scripts to automate parts of the SDLC — test generation, code review, documentation, or CI/CD tasks.
• Evaluation & guardrails — Understanding of how to evaluate AI-generated code or AI features for correctness, security, and reliability before they ship. For more information, visit our website at www.astranovamobility.com or reach out to us via our LinkedIn page.

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🏢 Astranova Mobility
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