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

You will work on a large-scale, multi-tenant B2B commercial execution platform used by enterprise go-to-market teams. It combines cloud software, capability benchmarking and advanced analytics to turn commercial strategy into measurable action - from account planning and partner management to pricing and sales performance.

This is a backend-focused Software Engineering role with a strong AI component. You'll build and operate the Python services behind the platform - APIs, data access, background processing - and you'll design and integrate the generative AI capabilities that sit on top of them: connecting LLMs with the platform's enterprise data, APIs and internal tools through prompt design, tool calling, workflow orchestration, guardrails and human review, all instrumented so that quality, latency and cost are measurable rather than assumed.

Daily use of AI development tools is part of how the whole team works, not an add-on. You're expected to use assistants like Claude Code across coding, testing, debugging and code review - and to validate everything they produce before it reaches production.

You'll be in regular, direct contact with product managers, designers and client-facing stakeholders, and you'll collaborate with a dedicated team of data scientists on the platform's harder analytical challenges: you own the product integration layer, they own the deeper modelling work. Security runs through everything. This is a multi-tenant SaaS platform handling sensitive enterprise client data, so every service you ship needs to respect tenant isolation, role-based access control and least-privilege data access - across the API, data and AI workflow layers.

What else you should know:

Team: Product Managers, UX Designers, Fullstack Engineers, Data Engineers, DevOps Engineers, AI Engineers, Data Scientists
You own the product-facing AI integration layer; deeper ML and data-science work is handled by dedicated specialists you collaborate with
AI-augmented development is a core expectation of the role, not optional - daily use of tools like Claude Code or Codex across coding, testing, debugging, and code review
Multi-tenant SaaS platform handling sensitive enterprise client data - a security-first mindset is expected at every layer
Agile, collaborative, impact-driven environment with close cooperation with business and client-facing stakeholders
Strong ownership culture and product mindset

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

More reasons to join us

Flexible working hours and approach to work: fully remotely, in the office or hybrid
Professional growth supported by internal training sessions and a training budget
Solid onboarding with a hands-on approach to give you an easy start
A great atmosphere among professionals who are passionate about their work
The ability to change the project you work on

Do we have a match?

As an AI Engineer, you must meet the following criteria:

6+ years of professional experience in software engineering with Python
Experience with FastAPI and Pydantic - endpoint design, dependency injection, models and validators, auth and error handling
Solid REST API design - versioning, predictable error semantics, token management, retries - with OAuth 2.0/Okta, JWT and role-based access control (RBAC)
Experience with automated testing with pytest
Strong SQL fundamentals (joins, GROUP BY, aggregate functions, query optimisation) and hands-on experience using PostgreSQL from Python - ORMs, database drivers, and a clear understanding of the trade-offs between synchronous and asynchronous drivers
Solid experience deploying and operating applications on Kubernetes
Experience with CI/CD pipelines (ideally GitHub Actions)
Hands-on experience integrating LLM APIs into production applications, including prompt design, tool/function calling, and safe handling of non-deterministic output
Experience using AI coding assistants such as Claude Code, Codex, or similar on a daily basis
Understanding of multi-tenant SaaS application security
Ability to evaluate AI features and monitor quality, latency, cost, and reliability
Product mindset and ownership - you identify problems, propose solutions, and take features from idea to production
Strong communication skills and good knowledge of English (minimum C1 level)

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

Evaluation and observability tooling for LLM features: eval harnesses, tracing, output scoring (LangSmith, Langfuse, Arize Phoenix or OpenTelemetry)
Systematic prompt optimisation - versioned prompts, A/B tests, DSPy
Agentic patterns beyond single LLM calls: multi-step orchestration, context management, guardrails and human-in-the-loop
Retrieval-augmented generation (RAG) and vector search (pgvector, Pinecone), plus frameworks such as LangChain or LangGraph
Experience with Snowflake or a comparable cloud data warehouse
Temporal, Socket.IO / WebSockets, Redis, pub/sub
Working knowledge of React / TypeScript — enough to make small front-end changes when a feature needs them

What else you should know:

Team: Product Managers, UX Designers, Fullstack Engineers, Data Engineers, DevOps Engineers, AI Engineers, Data Scientists
You own the product-facing AI integration layer; deeper ML and data-science work is handled by dedicated specialists you collaborate with
AI-augmented development is a core expectation of the role, not optional - daily use of tools like Claude Code or Codex across coding, testing, debugging, and code review
Multi-tenant SaaS platform handling sensitive enterprise client data - a security-first mindset is expected at every layer
Agile, collaborative, impact-driven environment with close cooperation with business and client-facing stakeholders
Strong ownership culture and product mindset

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

Technology stack:

Backend: Python, FastAPI, Pydantic, REST, pytest
Data: SQL, PostgreSQL, SQLAlchemy/async drivers, Snowflake, Redis
Cloud & DevOps: Azure / AWS, Docker, Kubernetes, GitHub Actions
AI: LLM APIs, prompt engineering, tool/function calling, LangChain/LangGraph, RAG, vector search (pgvector, Pinecone)
AI-assisted Development: Claude Code, Codex
AI observability & MLOps: LangSmith, Langfuse, Arize Phoenix, OpenTelemetry
Authentication & Security: OAuth 2.0, Okta, JWT, RBAC
Real-Time & Messaging: Temporal, Socket.IO / WebSockets

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