Must have

• Python

• Linux

• REST API

• CI/CD

• Git

• Data analytics

• Networks

• Network protocols

• Microservices

• LLM

• English (B2)

Nice to have

• Docker

• Kubernetes

Requirements description

Do we have a match?

As a Senior Python Software Engineer with LLM‑Driven Network Data Analytics you must meet the following criteria:

• At least 5 years of Python development experience focusing on data processing and visualizations using tools such as requests, pandas, matplotlib, altair, plotly, jupyter, pytest or similar.
• At least 1 year of hands‑on experience with LLM-based workflows, including prompt engineering, LangChain and LangGraph usage, embeddings, RAG/VectorDB integration, and automated or semi-automated testing of these workflows.
• Intermediate machine learning skills, especially in classification, clustering, and time-series analysis using scikit-learn or comparable frameworks.
• 1 year of experience applying AI techniques to network and IT infrastructure data— using knowledge on device behavior across layers and protocols, and leveraging their syslog and telemetry outputs for advanced observability.
• Intermediate proficiency with Linux, including shell scripting, environment setup, log inspection, and basic tooling use.
• English language skills at B2 level or higher.

Beyond the criteria above, we would appreciate the nice-to-haves:

• Familiarity with syslog processing workflows and log management tools like Splunk or Graylog.
• Knowledge of frameworks tailored to LLM output testing (DeepEval, BenchLLM, LangSmith, OpenAI Evals, TruLens).
• Proven experience in developing interactive Streamlit applications.
• Experience with containerization and orchestration, including Docker and Kubernetes for packaging, deployment, and scaling.
• Advanced ML capabilities, including deep learning or statistical modeling frameworks like PyTorch, TensorFlow, or statsmodels.

Offer description

Get to know us better

CodiLime is a software and network engineering industry expert and the first-choice service partner for top global networking hardware providers, software providers and telecoms. We create proofs-of-concept, help our clients build new products, nurture existing ones and provide services in production environments. Our clients include both tech startups and big players in various industries and geographic locations (US, Japan, Israel, Europe).

While no longer a startup, we have 250+ people on board and have been operating since 2011. We’ve kept our people-oriented culture. Our values are simple:

• Act to deliver.
• Disrupt to grow.
• Team up to win.

The project and the team

The project involves building LLM‑driven workflows to process data ingested into observability platforms. These workflows will classify, filter, and correlate events, enrich and transform log and metric data, and leverage LangChain and LangGraph to power root‑cause analysis.

What else you should know:

• The team consists of less than 7 people including an architect, project manager, and software and network engineers.
• We use SCRUM/Agile methodology.
• Our tech stack for the project includes: Python (requests, pandas, matplotlib, altair, plotly, pytest, scikit‑learn), LangChain, LangGraph, LLM (OpenAI, Gemini), Embeddings & Vector Database, Jupyter, Streamlit, docker, git, CI/CD pipelines
• The client is based in the US.

We work on multiple interesting projects at the time, so it may happen that we’ll invite you to the interview for another project, if we see that your competencies and profile are well suited for it.

Your responsibilities

1. Designing and orchestrating LLM-driven workflows tailored to syslog and telemetry analysis
2. Crafting clear, structured prompts to ensure well-formatted and reliable LLM outputs
3. Validating responses for relevance and accuracy, and fine-tuning prompts and workflows accordingly
4. Developing and integrating tools (e.g., topology lookups, telemetry APIs) and verifying their correct use by LLM agents
5. Building data transformation and enrichment pipelines for syslog and telemetry preparation
6. Proposing and iterating on workflow steps and feedback loops to continuously improve accuracy

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