• Designs, develops, and implements AI solutions and systems by applying advanced technical expertise to architect and code software applications. • Conduct system testing and debugging. • Collaborate with cross-functional teams. • Contribute to the overall technical direction and innovation of AI engineering projects.

• Apply deep platform knowledge to resolve inbound incidents from end users — including how-to questions, error messages, bugs, and enhancement requests • Own ticket lifecycle in ServiceNow — intake, categorization, resolution or escalation, and closure documentation • Identify patterns in ticket volume and surface recurring issues to the product team for upstream fixes or KB updates • Escalate issues that require engineering intervention with well-documented technical context to accelerate resolution • Develop and maintain internal technical documentation covering platform behavior, troubleshooting workflows, and support guidance • Author public-facing self-service content for end users; partner with the Artey team to ensure documentation is integrated and surfaced through the platform • Calibrate all documentation for the appropriate audience — translating technical detail into clear, actionable guidance for end users and Tier 1 agents where needed • Partner with the IT Service Center to ensure Level 1 agents have access to the knowledge base content and troubleshooting guidance needed to resolve basic issues • Develop and deliver targeted training sessions for business units experiencing confusion or low adoption • Collect user feedback from support interactions and synthesize it into actionable recommendations for the product team • Partner with the Build team on automation development, feature testing, QA, and deployment validation • Contribute to documentation and process design for new capabilities as they are developed

Role Description As an ML Engineer at NineTwoThree AI Studio, you will sit at the intersection of production-grade software engineering, advanced natural language processing, and client delivery. We build custom, high-impact AI systems for brands and startups across diverse industries (such as healthcare, logistics, and fintech). Instead of siloed academic research, this role demands a product-minded builder. You will: - Design, optimize, and deploy robust LLM applications, custom predictive analytics, and agentic workflows directly into our clients' software ecosystems. - Take absolute ownership of features from prototype to production. Technology Stack - Core Frameworks & Arch: Transformer models, modern LLM APIs (Anthropic Claude, OpenAI, AWS Bedrock, etc.), Open-Source LLMs. - Orchestration & Agentic Design: Experience designing LLM workflows, agentic systems, or retrieval pipelines using frameworks such as Langchain, LangGraph, LlamaIndex, or equivalent approaches. - Data & Search: Vector databases (Pinecone, pgvector, Milvus, Qdrant, etc.), SQL, and data engineering pipelines. - Traditional ML: Supervised and Unsupervised learning (Classification, Regression, Anomaly Detection). - Cloud & Infrastructure: AWS (Lambda, SageMaker, Bedrock, EC2) and modern DevOps/retraining pipelines. - Languages: Production-grade Python. Responsibilities - Architect & Build AI Features: Design and implement robust classical ML and generative AI solutions, striking the right balance between autonomous agentic architectures and deterministic pipelines. - Evaluate: Design and maintain evaluation frameworks to measure AI quality, reliability, safety, and business impact before and after deployment. - Integrate & Deploy: Partner closely with full-stack developers and DevOps to seamlessly integrate AI capabilities into client web and mobile applications using serverless architecture (e.g., AWS Lambda) or API endpoints. - Optimize for Production: Refine prompts, system instructions, and chunking strategies to balance accuracy, latency, token consumption, and data privacy. - Traditional Predictive Analytics: Clean and process unstructured or historical client data to train/fine-tune custom algorithms for specific business problems (such as forecasting, classification, or anomaly detection). - Collaborate & Communicate: Actively participate in client discovery sessions, translate ambiguous business requirements into viable technical scopes, and demo prototypes directly to stakeholder teams. - Maintain Engineering Excellence: Engage in constructive code reviews, implement rigorous validation patterns to test AI outputs, and contribute templates or runbooks to our internal AI knowledge base. Qualifications - 3+ years of experience engineering software with a strong focus on machine learning and natural language processing. - In-depth understanding of modern LLM architectures, context window mechanics, semantic search techniques, and the limitations of generative systems. - Experience building and operating production AI systems, including monitoring, evaluation, debugging, and iterative improvement. - Understanding of evaluation methodologies for LLM-based systems, including retrieval quality, hallucination detection, and task-specific performance measurement. - Exceptional Python coding skills and the ability to query, clean, and structure data efficiently. - Hands-on experience deploying ML or API services within cloud ecosystems, preferably AWS. - Comfortable taking ownership of ambiguous problems from initial discovery through production deployment and ongoing support. Requirements - Ability to drop into a completely new industry vertical, understand its data constraints, and spin up a working proof-of-concept within a few weeks. - Passion for seeing things ship and understanding why something is being built from a business value standpoint, not just what is being built. - Fluent written and spoken English. Comfortable interacting with client stakeholders and breaking down technical workflows into clear concepts. - Eagerness to experiment with and evaluate fast-emerging AI development tools, models, and frameworks. - Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field (or equivalent practical experience). Benefits - Annual paid vacation: 20 days off per year during the first 3 years, increasing to 25 days in later years. - Paid sick leave, 10 national holidays, and 2 company days off. - Well-being budget. - Maternity/paternity leave. - Reimbursement of expenses for professional development courses and certifications (up to 100% in agreement with Manager). - Hardware upon business needs. - Strong positive engineering culture, a tightly-knit team of professionals with a good sense of humor. What's The Process We value your time and ours and make the process fast and easy. Our interview process takes the following steps: - A short interview with the HR. - 2nd technical interview with ML Engineer and CTO (optional). - 3rd live-coding interview. - Offer.

Role Description A leading company in Mexico specializing in accounting software is looking for a highly skilled AI Engineer to join the team. Qualifications - 5 years of experience in artificial intelligence projects - 2 years in the implementation of autonomous agents or co-pilots - Fluent technical English - Experience working with business data in domains such as accounting, finance, payroll, billing, or ERP - Experience working with vector stores (Chroma, Weaviate, Pinecone) and RAG architectures Requirements - Handling frameworks such as LangChain, LlamaIndex, AutoGen, CrewAI, Semantic Kernel, or similar - Practical knowledge of MCP and A2A protocols, use of tools, memory management, and conversation status - Solid command of Python and experience with FastAPI, asyncio, Pydantic, and asynchronous architectures - Knowledge of MLOps: CI/CD, Docker, Kubernetes, agent monitoring, and automated retraining - Practical knowledge of other languages such as Golang, Java, or C# (.NET), especially in building high-performance components (Nice to Have) Responsibilities - Define, design, and supervise the technical architecture of solutions based on intelligent agents and LLMs, integrating tools such as LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent frameworks - Implement MCP (Model Context Protocol) and A2A (Agent-to-Agent) architectures to enable multi-agent coordination and autonomous flows within business environments - Work with the MLOps team and execution environments that enable continuous agent updating and deployment, including memory management, context, and long-term planning - Collaborate closely with product, UX, data, and backend teams to map business needs to intelligent agent architectures