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, transparent 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 strong 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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