AI Software Engineer

As an experienced AI Software Engineer, you will play a driving role within our multidisciplinary team (Data Scientists, DevOps, Consultants). You will be responsible for architectural design, development, and large-scale deployment of AI-based technology solutions for both internal and external clients. You will tackle complex, open-ended problems requiring sharp technical decisions, uncompromising engineering rigor, and a strong capacity for innovation.

Responsibilities :

• Architecture & Development: Design, architect, and develop complete, scalable features and platforms (APIs, business logic, databases, security).
• AI Engineering: Lead the implementation of complex AI systems in production: advanced RAG pipelines, LLM agent fleets, model fine-tuning and integration (OpenAI, Anthropic, open-source LLMs).
• Quality & Clean Code: Champion best practices (SOLID principles, Clean Architecture, design patterns) and conduct thorough code reviews.
• Reliability & Testing: Define the testing strategy (TDD, unit, integration, e2e, stress tests) to ensure flawless deployments.
• MLOps & Cloud: Deploy, monitor, and optimize models and code in dedicated environments with robust CI/CD pipelines.
• Client Facing & Advisory: Act as a technical expert with clients. Understand their business challenges, break down complex concepts, and defend your architectural decisions.
• Mentoring: Support the growth and skill development of junior developers and DataLab interns.

Required Skills :

Fundamentals — Full Mastery Expected

• System Design & Architecture: Ability to design distributed, scalable, and resilient systems. Strong command of architectural trade-offs.
• Data Structures & Algorithms: Solid algorithmic expertise for performance optimization (Big-O, streaming and batch data processing).
• Clean Code & Patterns: Strict application of SOLID principles and mastery of design patterns (including microservices and event-driven architectures).
• CI/CD & DevOps: Advanced Docker, orchestration (Kubernetes is a plus), GitHub Actions / GitLab CI, Terraform / Infrastructure as Code.
• APIs & Databases: Design of robust APIs (REST, gRPC, GraphQL). Optimization of relational (PostgreSQL) and non-relational databases (MongoDB, Redis, graph databases).

AI & Data — Technical Expertise:

• LLM Integration & Ops (LLMOps): Advanced API integration, complex prompt engineering, token/cost management, streaming, continuous monitoring, and evaluation of models in production.
• RAG & Agent Ecosystem: Proficiency with LangChain, LlamaIndex, or custom solutions. LangGraph. Large-scale implementation of Vector Stores (FAISS, Pinecone, Qdrant, Milvus). Advanced chunking and reranking strategies.
• Information Retrieval: Solid understanding of search algorithms (TF-IDF, BM25, Cross-Encoders) and search engines (Elasticsearch).

Tech Stack — Tech Agnostic, with Strong Appetite for:

Python · TypeScript / Node.js · Java / Spring · FastAPI / Django · React · PostgreSQL · Redis · Vector DBs

AWS / GCP / Azure · Docker / Kubernetes · Git / CI/CD · LangChain / LlamaIndex · OpenAI API · Elasticsearch · Kafka / RabbitMQ

• Engineering degree or Master's (Computer Science, Mathematics, Data Science) with 3 to 6 years of relevant experience.
• Proven track record: You have already designed, developed, and deployed software solutions integrating AI (Machine Learning, NLP, or GenAI) with measurable business impact.
• Problem-solving & Autonomy: Ability to take a vague business need and turn it into a solid technical architecture and finished product.
• Communication & Leadership: Strong interpersonal skills to engage with demanding clients and the ability to rally a technical team.
• Technological Watch: Insatiable curiosity for a fast-moving AI ecosystem.