Role Description
We are looking for a Senior AI Engineer to design and build production-grade AI applications and services using Python. In this role, you will turn business and AI use cases into reliable, scalable technical solutions, working with LLMs, backend services, APIs, and enterprise systems.
Responsibilities
- Design and build production-grade AI applications and services using Python
- Translate business requirements and AI use cases into practical technical solutions
- Build backend services, APIs, workflows, and integrations that incorporate LLMs and other AI capabilities
- Design evaluation frameworks and test cases to measure the quality and reliability of AI outputs
- Develop techniques to improve the consistency and predictability of LLM-powered features
- Implement observability, instrumentation, tracing, and monitoring for AI systems
- Build reusable components for prompting, model interaction, evaluation, retrieval, and orchestration
- Develop automated tests and CI/CD pipelines for AI applications
- Integrate AI applications with enterprise data platforms, APIs, and business systems
- Diagnose and resolve issues in deployed applications and support production workloads
- Contribute to architecture and technology decisions across AI products
- Review engineering work and establish best practices for Python and AI application development
- Work directly with clients and stakeholders to refine requirements, evaluate trade-offs, and shape solutions
About Trinetix
Established in 2011, Trinetix is a dynamic tech service provider supporting enterprise clients around the world. Headquartered in Nashville, Tennessee, we have a global team of over 1,000 professionals and delivery centers across Europe, the United States, and Argentina. We partner with leading global brands, delivering innovative digital solutions across Fintech, Professional Services, Logistics, Healthcare, and Agriculture. Our operations are driven by a strong business vision, a people-first culture, and a commitment to responsible growth.
Hybrid or full remote work environment.
Must-Haves
- 5+ years of experience in software engineering, backend engineering, applied AI, or related fields
- Strong Python fundamentals and experience building maintainable, production-grade Python applications
- Experience designing and deploying AI or LLM-powered products beyond proof-of-concept or notebook environments
- Experience building backend services and APIs using Python
- Strong understanding of software engineering fundamentals, including modular design, testing, version control, and CI/CD
- Hands-on experience evaluating LLM outputs and designing repeatable test cases for AI applications
- Experience with observability, instrumentation, logging, tracing, or monitoring of production applications
- Understanding of the challenges involved in making probabilistic AI systems reliable and predictable
- Experience integrating applications with external APIs, data sources, and enterprise systems
- Ability to reason from first principles and solve unfamiliar technical problems rather than relying solely on frameworks or tools
- Strong communication skills and experience working directly with technical and business stakeholders
- Ability to balance hands-on delivery with technical guidance and solution design
Nice-to-Haves
- Experience with RAG, semantic search, embeddings, vector databases, or other retrieval-based AI architectures
- Experience building agentic or multi-step LLM workflows
- Familiarity with structured LLM outputs, tool calling, and schema-driven application patterns
- Experience with AI evaluation, experimentation, or observability platforms
- Experience with Microsoft Azure AI Foundry, Microsoft Fabric, Databricks, or similar enterprise platforms
- Experience with cloud infrastructure, containerisation, and production deployment of Python services
- Familiarity with modern Python frameworks and tooling for APIs, data validation, testing, and dependency management
- Experience integrating AI applications with enterprise data platforms and semantic layers
- Exposure to consulting, financial services, manufacturing, or other enterprise environments
- Experience supporting production applications used by external clients or business-critical teams
