AI Engineer, Freelance

Equativ (formerly Smart)

AI Engineer developing and deploying generative AI solutions at Equativ. Engaging with operational teams and building production AI agents to meet customer needs.

Posted 5/30/2026contractParis • 🇫🇷 FranceMid-LevelSeniorWebsite

Tech Stack

Tools & technologies

CloudKubernetesPython

About the role

Key responsibilities & impact

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.

Requirements

What you’ll need

Master's degree in Computer Science, Data Science, or a similar technical field.
3+ years as a Python Developer or ML Engineer, with a recent focus on deploying LLM-powered solutions in production.
Mastery of Python for enterprise-level development, strong knowledge of core software engineering principles, and hands-on experience with AIOps/MLOps (unit tests, CI/CD, Git, observability). You should be comfortable being your team's reference on these topics.
Proven experience building production-ready agent workflows, orchestration layers, or platforms using Python frameworks (ADK, Langchain, LangGraph).
Practical experience with modern cloud platforms (Vertex AI, Kubernetes or equivalents) for deploying scalable GenAI services.
Strong versatility and a demonstrated willingness to work across the full stack and across functional expertises.
Entrepreneurial mindset, high autonomy, and the ability to turn ambiguous, high-level business goals into concrete, efficient GenAI features.
Fluent technical English (written and verbal) — mandatory.

Benefits

Comp & perks

Flexible work arrangements
Professional development
Dynamic, flexible workplace

ATS Keywords

✓ Tailor your resume

Applicant Tracking System Keywords

Tip: use these terms in your resume and cover letter to boost ATS matches.

Hard Skills & Tools

PythonAIOpsMLOpsunit testsCI/CDGitobservabilityagent workflowsorchestration layersLLM-powered solutions

Soft Skills

entrepreneurial mindsethigh autonomystakeholder engagementcommunicationversatility

Certifications

Master's degree in Computer ScienceMaster's degree in Data Science