AI Engineer, Senior

Logiciel Solutions | AI-Powered Teams That Feel In-House

. Design and implement multi-step AI agents, reasoning flows, and automation workflows interacting with internal and external systems.

Posted 10/28/2026full-timeRemote • United StatesSeniorWebsite

Core Competencies

Role fit

Core Competencies

Use this summary to align your resume positioning with the role.

Demonstrates expertise in designing and implementing multi-step AI agents and workflows, with strong proficiency in TypeScript/Node and Python. Capable of integrating complex systems and ensuring reliability and observability in production environments.

Highest-signal resume keywords

Applied AI Systems DevelopmentTypeScript/Node ProficiencyPython ProgrammingWorkflow OrchestrationRetrieval Workflows Expertise

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

AI Agent DesignWorkflow AutomationRetrieval PipelinesKnowledge-Grounding WorkflowsProduction-Ready ServicesEvaluation PipelinesVector DatabasesModel MonitoringCustom Orchestration PatternsLLMOps

Soft Skills

Strong Communication SkillsMentoring EngineersProblem-Solving

Tools & Technologies

APIsEvent SystemsMCP ProtocolA2A ProtocolNo-Code ToolsWorkflow Engines

Industry Keywords

Applied AI SolutionsSoftware EngineeringBackend FundamentalsPrototype DevelopmentTechnical Trade-Offs

Tech Stack

Tools & technologies

Node.jsPythonTypeScript

About the role

Key responsibilities & impact

Design and implement multi-step AI agents, reasoning flows, and automation workflows interacting with internal and external systems.
Implement agent logic, tooling, and workflow orchestration.
Develop retrieval pipelines and knowledge-grounding workflows.
Build structured extraction flows using LLMs and transformation logic.
Convert prototypes into production-ready services with monitoring and predictable behavior.
Ensure agents are explainable, observable, and have defined failure and fallback paths.
Define architectures for applied AI solutions using TypeScript/Node or Python.
Integrate agents with APIs, databases, event systems, and existing product surfaces, including protocols such as MCP or A2A.
Establish patterns for prompt management, model selection, retrieval, and verification.
Implement evaluation pipelines to monitor accuracy, drift, and reliability.
Build reusable modules and internal tooling to accelerate future AI development.
Build functional prototypes and demos to validate ideas.
Assess feasibility, complexity, and risks of proposed AI features.
Collaborate with sales and delivery teams on scope and technical approach.
Advise on trade-offs between model and architecture choices.
Help create realistic timelines and delivery plans.
Mentor backend engineers on AI patterns, workflows, and integration techniques.
Introduce practices for testing, evaluation, observability, and versioning.
Share knowledge on prompting, model behavior, and tool usage.
Establish standards for applied AI solution design and delivery.

Requirements

What you’ll need

5+ years of experience stated in the experience range.
6–15 years of software engineering experience with strong backend fundamentals.
Proven experience building and shipping applied AI systems or agents in production.
Strong hands-on experience with TypeScript/Node and Python.
Experience with agent frameworks or custom orchestration patterns.
Strong understanding of retrieval workflows, embeddings, chunking, and evaluation methods.
Experience working with vector databases and related tooling.
Decent understanding of underlying infrastructure and production operations, including LLMOps.
Ability to design and integrate multi-step workflows with external systems and APIs.
Comfortable working from prototype to production with attention to reliability and observability.
Ability to break down ambiguous ideas and propose clear, technically grounded solutions.
Strong communication skills, especially when explaining feasibility and technical trade-offs.
Nice to have: experience with speech-to-text or text-to-speech pipelines.
Nice to have: familiarity with workflow engines or orchestration tools.
Nice to have: experience with evaluation frameworks or custom benchmarking pipelines.
Nice to have: exposure to model monitoring, versioning, or lightweight MLOps practices.
Nice to have: prior involvement in pre-sales or client-facing technical discussions.
Nice to have: experience mentoring engineers on applied AI practices.
Nice to have: experience with no-code tools like n8n.

Benefits

Comp & perks

Become the foundational hire for Applied AI at Logiciel.
Own high-impact projects from design to deployment.
Influence architecture, patterns, and future team growth.
Work in a culture that values engineering clarity, autonomy, and tangible results.