AI applied Software Engineer
Engineers who have lived through the evolution of computer science and data technologies, now transforming how enterprises work with LLMs and AI agents. Take cutting-edge AI from idea to production with your own hands.
What you'll do
- Build AI AgentsDesign and develop products that fundamentally change how enterprises work, using LLMs and agent technologies
- Prove It with Working CodeTurn ideas into prototypes in days and show value right in front of clients
- Stay on the Cutting EdgeRAG, multi-agent systems, fine-tuning — chase the evolution of AI technology and ship it into products immediately
- Ship to ProductionNot just demos. Build production systems with scale, reliability, and security
- Build as a TeamWork as one with designers, SREs, and Delivery Leads to grow a product together
- Grow with ClientsNot just handing over technology, but building a state where clients wield AI on their own
What we're looking for
Required Experience & Skills
Problem Framing
The ability to transform a client request like “we want a chatbot” into a technical question: “At which step of which workflow, and what kind of information should AI process to change the operational structure?” Refusing to accept ambiguous requirements, concretizing hypotheses through working prototypes, and defining the priority of technical problems to solve
Problem Solving
The ability to read through documentation, source code, and error logs in unfamiliar tech stacks and constraints, reaching a working implementation with minimal validation. Not “waiting for the perfect design” but “building something that works quickly and learning from feedback.” Calmly isolating root causes when unexpected failures occur and finding solutions together with the team
Communication
Discussing product requirements with Delivery Leads, refining UI implementation details with designers, aligning operational design with SREs — explaining the rationale behind technical decisions in terms suited to each person’s domain. Providing constructive feedback in code reviews and raising the technical capabilities of the entire team
Software Engineering
Experience designing, building, and operating production systems. Language-agnostic, but with deep understanding of code quality, performance, and maintainability and a sense of ownership. Writing code that “survives operations” not just “works,” taking end-to-end responsibility from testing and documentation through deployment
Languages & Frameworks
Hands-on experience across multiple languages and frameworks including Python (FastAPI, Django, Flask), TypeScript / JavaScript (React, Next.js, Node.js), Java (Spring Boot), Go, and Rust. Selecting the optimal technology for each project’s requirements without depending on a single language. Data access layer design experience with SQL and GraphQL
Architecture & Design
Production experience with microservices, event-driven architecture, and API design (REST, gRPC, GraphQL). Ability to explain why a given architecture was chosen and articulate its trade-offs. Practicing test-driven development and continuously pursuing designs that are resilient to change
Data & Storage
Understanding when to use RDBMS (PostgreSQL, MySQL), NoSQL (MongoDB, DynamoDB), Redis, and Elasticsearch for different use cases. Designing semantic search with vector stores (Pinecone, Weaviate, Chroma, pgvector). Implementation experience with asynchronous processing patterns using messaging systems such as Kafka, RabbitMQ, and SQS
ML/AI Fundamentals
Understanding of machine learning fundamentals — supervised/unsupervised learning, model training pipelines, loss functions, evaluation metrics, and transformer architecture. Ability to reason about model behavior, debug AI system issues from first principles, and make informed architecture decisions for AI-integrated applications
Nice to Have
AI & LLM
Product development experience using LLMs such as OpenAI GPT-4 / o-series, Claude (Anthropic API), and Gemini. Building RAG pipelines, fine-tuning, and AI Agents (CrewAI, AutoGen) with LangChain, LlamaIndex, Hugging Face Transformers, and vLLM. Practicing evaluation and quality management with RAGAS, LangSmith, and similar tools
Infrastructure & DevOps
Container orchestration with Docker and Kubernetes, production operations on AWS / GCP / Azure. Infrastructure management with IaC tools such as Terraform, CI/CD pipeline design and operations with GitHub Actions, GitLab CI, and similar. Driving automation from development through deployment
Domain
Experience building and operating enterprise AI products. Understanding client business processes, explaining technical options in business context, and co-developing products together
Interested in this role?
Tell us what drives you and what you want to build.