AI Engineer

Established in 2011, Trinetix is a dynamic tech service provider supporting enterprise clients around the world. Headquartered in Nashville, Tennessee, it has a global team of over 1,000 professionals and delivery centers across Europe, the United States, and Argentina. The company partners with leading global brands, delivering innovative digital solutions across Fintech, Professional Services, Logistics, Healthcare, and Agriculture. Trinetix operates with a strong business vision, a people-first culture, and a commitment to responsible growth, actively engaging in CSR activities and adhering to international principles for sustainable development and business ethics.

• 5+ years of experience in data, analytics, machine learning, or AI-driven environments.
• Strong hands-on expertise with LLMs, GenAI frameworks, ML/analytics pipelines, and vector databases.
• Proficiency in Python and experience delivering production-grade AI solutions.
• Solid understanding of enterprise AI patterns (RAG, observability, agent workflows, model evaluation).
• Knowledge of data pipelines, event-driven architectures, and system-to-system integration.
• Familiarity with data science practices: feature engineering, statistical modeling, experiment design, and hypothesis-driven analysis.
• Ability to work with structured and unstructured data, conduct exploratory analysis, and guide AI solution design.
• Experience collaborating across engineering, architecture, IT, and operations teams.
• Strong communication skills to simplify AI concepts for non-experts.
• Curiosity and adaptability to learn emerging AI tools and telecom domain concepts.

Nice to have:

• Experience with RAG-based systems and advanced observability frameworks.
• Familiarity with MLOps practices (model monitoring, validation, lifecycle management).
• Knowledge of agent workflows and orchestration patterns in enterprise AI.
• Exposure to telecom-specific AI use cases (network telemetry, predictive maintenance, intelligent automation).
• Cloud-native AI platforms and scalable data infrastructure.

• Design and implement AI/ML pipelines, including LLM-based applications, GenAI frameworks, and vector retrieval systems.
• Build production-grade AI integrations in Python, ensuring scalability and reliability.
• Apply enterprise AI patterns such as RAG, observability, agent workflows, and model evaluation.
• Architect secure, scalable integrations between cloud platforms and enterprise systems.
• Develop data pipelines, event streams, and observability frameworks to support AI at scale.
• Collaborate with Data Scientists on feature engineering, model evaluation, and experiment design.
• Contribute to AI-driven telecom solutions by learning and applying concepts in IP routing, EVPN/L3VPN, telemetry, and optical networking.
• Translate technical capabilities into measurable business impact through KPIs, ROI analysis, and product-focused thinking.