AI Engineer

• Develop cutting-edge GenAI solutions, incorporating the latest techniques from Databricks AI research to solve customer problems
• Own production rollouts of consumer and internally facing GenAI applications
• Serve as a trusted technical advisor to customers across a variety of domains
• Present at conferences such as Data + AI Summit, recognized as a thought leader internally and externally
• Collaborate cross-functionally with the product and engineering teams to influence priorities and shape the product roadmap

AI Engineer – Forward Deployed Engineer

• Build production agents — design, develop, and deploy goal-oriented AI agents and multimodal/conversational experiences using frameworks like Google ADK, Langchain, LangGraph, combined with orchestration tools like n8n where appropriate.
• Own the production lifecycle of what you ship — establish robust AIOps/AgentOps practices (monitoring, versioning of agent blueprints, evaluation pipelines, reliability) within your team's scope.
• Contribute to the shared Agentic Platform (Core pillar) — gateways, evaluation frameworks, observability, MaaS/AaaS APIs — so feature teams build faster on solid foundations.
• Build agent-side integrations (Feature pillar) — develop the MCP servers and backend services that agents need to interact with enterprise systems, in partnership with other R&D teams (and picking up the work yourself when a partner team doesn't have bandwidth).
• Be the Python referent in your team — own production-quality Python, enforce strong SWE principles (unit tests, CI/CD, Git, code review), and bring AIOps/MLOps best practices wherever you sit.
• Build a working expertise on agentic design patterns (eval, guardrails, multi-agent orchestration) and share it with AI champions and AI builders across the company as the GenAI Center of Expertise takes shape.
• Engage with stakeholders — talk to internal teams to understand operational pain points and translate them into measurable GenAI solutions. You don't lead cross-team architecture, but you should be credible across the org.

AI Engineer, Freelance

• Development and enhancement of AI applications
• Contribute to the development of the central AI service: new features, tools integration, robustness and performance
• Evolve the conversational agent and make its services easier to use by other applications
• Participate in data science projects: modeling, evaluation, and production deployment
• Ensure code quality and maintainability of solutions (tests, documentation, reviews) within Azure DevOps
• Capture and translate business requirements
• Engage with business teams to identify new needs and AI use cases
• Evaluate and implement solutions to these needs in a way that is coherent with the ecosystem
• Propose solutions adapted to cost, time, and complexity constraints
• Monitor developments in Azure AI models and services, assessing their relevance to our projects
• Contribute to improving team practices: performance, cost control, security

AI & Data Engineer

• Co-design with company executives (C‑level) the Data & AI vision for the clients we support.
• Translate that vision into technology roadmaps, target architectures and implementation strategies.
• Ensure alignment between business objectives, technical constraints and measurable value creation.
• Serve as the technical authority on projects: stack selection, architecture design, best practices, scalability and security.
• Oversee the design and implementation of bespoke solutions: data pipelines, cloud platforms, AI models, APIs, middleware and MLOps.
• Ensure technical consistency across projects: version upgrades, maintenance, performance and operational robustness.
• Lead multidisciplinary teams to design, develop and deploy end-to-end Data & AI solutions.
• Guarantee the technical quality, performance and reliability of delivered solutions.
• Stay at the forefront of technological advances: new algorithms, frameworks, architectures, AI/ML tools and innovations (e.g., Databricks, Snowflake).
• Help shape and scale the custom-build function to take it to the next level.
• Mentor, coach and upskill teams of data engineers, ML engineers, developers and data scientists.

Senior Tech Lead – AI Engineering

• Build Scalable & Reliable Architecture
• Design and implement scalable, maintainable end-to-end features (specification, development, testing, documentation, support)
• Ensure resource consumption is optimal when necessary
• Drive architectural decisions that support long-term business growth
• Ensure clean code, automated testing, and CI/CD best practices
• Optimize APIs, data models, and microservices for performance
• Collaborate Cross-Functionally
• Partner with Product Managers to align business goals and technical feasibility
• Collaborate with DevOps to ensure stability and efficient deployments
• Facilitate technical discussions and drive knowledge-sharing across teams
• Improve System Performance & Reliability
• Continuously improve system performance, observability, and fault tolerance
• Apply technical refactoring efforts and reduce legacy risks
• Embed security and compliance into development practices
• Mentor & Lead Engineering Teams
• Act as a mentor and technical reference for engineers
• Set high standards for code quality, documentation, and development practices
• Drive Team Growth & Talent Development
• Take part in recruiting top talent and leading technical interviews
• Contribute to onboarding and continuous learning programs
• Foster a collaborative, inclusive, and high-performing engineering culture.

AI Engineer – Software Engineering

• Collaborate with stakeholders (product, marketing, operations, commerce, logistics) to analyze business challenges, define precise problem statements, and model them as tractable machine learning tasks (e.g., defining classification vs. ranking objectives, specifying metric targets).
• Design, implement, and maintain scalable machine learning pipelines, emphasizing Large Language Models (LLMs) and generative architectures.
• Fine-tune custom LLMs for specific use cases for cost and performance optimizations.
• Monitor AI systems performance in real time (latency, accuracy, drift) and establish automated retraining processes to maintain high quality, robustness, and fairness.
• Partner with PMs, UX/UI designers and frontend/backend engineers to embed AI capabilities into Veepee’s Information System.

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AI Engineer – Forward Deployed Engineer

AI Engineer, Freelance

AI & Data Engineer

Senior Tech Lead – AI Engineering

AI Engineer – Software Engineering

Senior AI Engineer