Senior AI Automation Engineer

Role Overview

Join our team and help shape the future of connectivity indoors and outdoors. Together, let’s push the boundaries of technology and advance sustainable networks worldwide.

What You Will Do

Design, build, and run LLM-powered and agentic systems that automate triage, classification, and decision support for our operations and support teams.

Why It Might Be a Fit

You will be expected to pair strong backend engineering with practical GenAI skills, and will be based in Bangalore, Karnataka.

Requirements

• Bachelor’s or Master’s degree in Computer Science, Information Science, Electronics Engineering, or a related field.
• 3 to 5 years of professional software engineering experience, including at least 1 to 2 years building LLM-powered applications that run in production.
• Track record of taking AI features from prototype to production, with measurable impact on quality, speed, or cost.
• Hands-on experience with LLM APIs (Anthropic Claude, OpenAI) including prompt engineering, tool/function calling, and structured outputs.
• Practical experience building RAG systems: chunking, embeddings, vector databases (FAISS, Chroma, pgvector), hybrid search, and re-ranking.
• Experience with agent frameworks such as LangGraph, the Claude Agent SDK.
• Familiarity with LLM evaluation and guardrail techniques: golden datasets, LLM-as-judge, red-teaming, and mitigation of hallucination and prompt injection.
• Daily use of AI coding assistants such as Claude Code, GitHub Copilot, or Cursor.
• Strong Python skills, including asyncio, type hints, and testing with pytest.
• Experience designing REST APIs with frameworks such as FastAPI or Flask.
• Experience with async, event-driven pipelines and queues (e.g., Celery, Kafka, Redis), plus working knowledge of SQL and NoSQL data stores.
• Experience with CI/CD (Jenkins, GitHub Actions, or GitLab CI), Docker, Kubernetes.
• Working knowledge of secrets management (e.g., Vault, AWS Secrets Manager), role-based access control, and dependency and container scanning (e.g., Snyk, Trivy).
• Experience with observability tooling (e.g., OpenTelemetry, Prometheus, Grafana, Langfuse) for tracing, metrics, and cost tracking of LLM applications.
• Hands-on experience with a major cloud platform, preferably AWS.

Tagged as: Python, SQL