Role Description En Coforge estamos buscando un Mid AI Engineer (#21869) con el siguiente perfil. - Liderar el diseño end-to-end, desarrollo y optimización de agentes de IA, prompts y flujos conversacionales sobre Copilot 365, Copilot Studio y Azure AI Foundry. - Definir estándares, frameworks de prompting, evaluación y guardrails alineados a políticas de gobernanza, seguridad y compliance. - Orquestar la interacción entre modelos LLM y herramientas de integración en arquitecturas event-driven. - Asegurar la escalabilidad, performance, resiliencia y optimización de costo de las soluciones de IA. - Diseñar estrategias de seguridad, auditoría, control de acceso y trazabilidad (PII, RBAC, data boundaries). - Liderar la medición de impacto, adopción y madurez de soluciones de IA en producción. - Coordinar con áreas de arquitectura, seguridad, infraestructura y equipos técnicos para asegurar alineamiento end-to-end. Qualifications - +5 años de experiencia en soluciones de IA, automatización o bots y +3 años en entornos enterprise. - Experiencia en Copilot 365, Copilot Studio y GitHub Copilot. - Experiencia con Azure AI Foundry y RAG. - Conocimiento de APIs REST y arquitecturas event-driven. - Experiencia con Azure Functions o Logic Apps (deseable). - Conocimiento de Python o JavaScript (deseable). - Experiencia en diseño de arquitecturas de agentes autónomos y event-driven. - Conocimiento en MLOps o IA Ops y gobierno de modelos. - Experiencia en seguridad avanzada, compliance y gobernanza de datos. Requirements - Experiencia en frameworks de agentes (MAF u otros similares). - Experiencia en escalamiento de soluciones de IA a producción. - Experiencia en definición de estándares de IA generativa a nivel organizacional. - Experiencia en mentoría de perfiles junior o semi senior. - Conocimiento en optimización de performance, costo y calidad de soluciones de IA. Company Description En Coforge, contratamos profesionales únicamente en función de sus habilidades y calificaciones, y no discriminamos en función de la edad, discapacidad, religión, género, orientación sexual, estado socioeconómico o nacionalidad.

Role Description We are seeking a visionary and highly technical Staff AI Engineer to join our EngOps team within the new Enterprise AI squad . This role is central to our next phase of growth, focusing on designing, building, and scaling the core autonomous AI infrastructure that powers both our customer-facing product and internal engineering operations. In this role, you will be the architect behind our "Maestro" (our central AI orchestrator) and its distributed execution systems. You will be instrumental in evolving our WhatsApp-based AI platform into a sophisticated, multi-agent enterprise system capable of reasoning, planning, and automating complex, multi-step tasks across critical internal and product functions (e.g., loans, payments, fraud). You will act as a foundational technical anchor for EngOps, helping shape the future architecture of this new squad and analyzing what the division needs next as we scale. If you thrive at the intersection of deep software infrastructure, operational excellence, and agentic AI, this is your mission. Responsibilities - Maestro & Orchestration Architecture: Architect, develop, and maintain the core infrastructure for our central AI orchestrator ("Maestro") and its connected execution agents to optimize engineering and enterprise operations. - Infrastructure-First AI Development: Lead the integration of AI agents into core product and operational infrastructure. Own the full lifecycle from containerization (Docker/Kubernetes) to CI/CD deployment, ensuring high availability, security, and post-launch stability. - LLMOps & Observability: Establish production-grade monitoring, tracing, and logging for complex agentic workflows within the EngOps ecosystem to track reasoning paths, tool-calling success rates, and latency bottlenecks. - Advanced Evaluation Pipelines: Design automated "evals-as-code" using LLM-as-a-judge, semantic similarity testing, and adversarial benchmarking to ensure agent safety, reliability, and groundedness. - Performance & Cost Engineering: Optimize RAG pipelines and agent loops for production constraints, implementing caching strategies, prompt compression, and model routing to balance inference costs with high performance. - Strategic Technical Leadership: Define operational best practices for AI deployment, asynchronous programming, and structured data validation. Help map out future team and infrastructure needs. Qualifications - 8+ years of hands-on experience in software engineering, with a strong, proven background in infrastructure and systems development, and at least 2 years dedicated to building and deploying production-grade AI applications focused on LLMs. - Exceptional expertise in Python and deep knowledge of System Architecture. Mastery of core LLM APIs and agentic frameworks. - Proven experience implementing complex agentic systems, including RAG, tool-calling, vector databases, and advanced memory/state management. - High ownership and comfort working in an early-stage squad environment—someone who can independently analyze operational bottlenecks, propose architectural solutions, and code them. - Nice to have: Hands-on experience or active experimentation with Open CLAW, Hermes, or advanced open-source AI Agent frameworks. Requirements - 8+ years of hands-on experience in software engineering. - Strong background in infrastructure and systems development. - At least 2 years dedicated to building and deploying production-grade AI applications focused on LLMs. - Exceptional expertise in Python. - Deep knowledge of System Architecture. - Proven experience implementing complex agentic systems. - High ownership and comfort working in an early-stage squad environment. Benefits - Competitive salary - Initial stock options grant - Annual performance bonus - Health, dental, and vision plans - 401(k) with employer match - Continuous learning opportunities - Unlimited PTO - Paid parental leave - Empowering opportunities for growth in a dynamic entrepreneurial environment Equal Opportunity Employer At Félix, we are committed to providing equal employment opportunities to all qualified employees and applicants without regard to race, religion, nationality, sex, sexual orientation, gender identity, age, or disability. This policy applies to all terms and conditions of employment, including recruitment, hiring, placement, promotion, training, compensation, benefits, and termination.

• 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