Senior AI Platform Engineer at Endava

Monterrey, Nuevo León, Mexico -

Full Time

Start Date

Immediate

Expiry Date

16 Aug, 26

Salary

0.0

Posted On

18 May, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI Solution Engineering, AI Platform Architecture, MLOps, Cloud Infrastructure, Production-Grade AI Systems, Python, LLM Applications, Generative AI, RAG Architectures, Vector Search Solutions, Cloud-Native Architectures, Docker, Kubernetes, APIs, Microservices, Event-Driven Architectures

Industry

IT Services and IT Consulting

Description

Company Description Technology is our how. And people are our why. For over two decades, we have been harnessing technology to drive meaningful change. By combining world-class engineering, industry expertise and a people-centric mindset, we consult and partner with leading brands from various industries to create dynamic platforms and intelligent digital experiences that drive innovation and transform businesses. From prototype to real-world impact - be part of a global shift by doing work that matters. Job Description We are looking for a highly skilled and hands-on Senior AI Engineer / AI Platform Engineer to join our technology organization and help accelerate the adoption of scalable AI capabilities across the business. This role combines strong expertise in AI solution engineering with deep understanding of AI platform architecture, MLOps, cloud infrastructure, and production-grade AI systems. The ideal candidate will be capable of designing, building, deploying, and operating AI-powered solutions end-to-end while contributing to the evolution of our internal AI platform ecosystem. You will work closely with engineering, product, data, and architecture teams to deliver secure, scalable, observable, and business-oriented AI solutions leveraging LLMs, RAG pipelines, AI agents, and modern cloud-native architectures. This is a key role for someone passionate about turning AI innovation into reliable enterprise capabilities. Key Responsibilities Design, develop, and deploy scalable AI solutions using modern AI/ML frameworks and LLM technologies. Build and maintain AI platform capabilities that enable rapid experimentation, deployment, monitoring, and governance of AI workloads. Develop and optimize Retrieval-Augmented Generation (RAG) pipelines and AI agents for enterprise use cases. Integrate and orchestrate LLM providers and AI services (OpenAI, Anthropic, Azure OpenAI, etc.). Create reusable AI services, APIs, SDKs, and platform components for internal engineering teams. Implement observability, evaluation, and monitoring strategies for AI systems and model performance. Collaborate with cross-functional teams to define AI use cases, technical requirements, and delivery roadmaps. Contribute to MLOps and AI Ops practices, including CI/CD pipelines, model lifecycle management, infrastructure automation, and governance. Ensure AI solutions meet enterprise standards for security, scalability, resilience, compliance, and cost efficiency. Drive best practices around prompt engineering, model evaluation, guardrails, and responsible AI. Mentor engineers and contribute to the technical growth of the broader engineering organization. Stay up to date with emerging AI technologies, frameworks, and industry trends. Qualifications Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Science, or related field. 5+ years of experience in software engineering, AI engineering, ML engineering, or platform engineering roles. Strong programming skills in Python and experience building production-grade backend systems. Hands-on experience with LLM applications, generative AI, and orchestration frameworks such as LangChain, LangGraph, Semantic Kernel, or similar. Experience designing and implementing RAG architectures and vector search solutions. Solid understanding of cloud-native architectures and public cloud platforms (AWS, Azure, or GCP). Experience with containers and orchestration technologies such as Docker and Kubernetes. Familiarity with MLOps practices, CI/CD automation, infrastructure-as-code, and observability tooling. Experience working with APIs, microservices, distributed systems, and event-driven architectures. Strong understanding of software engineering best practices, including testing, version control, and secure development. Excellent communication and collaboration skills. Advanced English communication skills. Nice to Have Experience with AI governance, evaluation frameworks, and responsible AI practices. Knowledge of vector databases and embedding pipelines. Experience with workflow automation/orchestration platforms such as N8N or Airflow. Exposure to multi-agent systems and autonomous AI workflows. Experience building internal developer platforms or AI enablement tooling. Certifications in cloud, AI, or machine learning technologies. Experience in financial services or enterprise-scale environments. What We Value Ownership mindset and strong delivery orientation. Pragmatic problem-solving skills. Passion for innovation balanced with engineering discipline. Collaborative leadership and mentoring capabilities. Curiosity and continuous learning. Ability to thrive in fast-evolving technology environments. Additional Information Discover some of the global benefits that empower our people to become the best version of themselves: Finance: Competitive salary package, share plan, company performance bonuses, value-based recognition awards, referral bonus; Career Development: Career coaching, global career opportunities, non-linear career paths, internal development programmes for management and technical leadership; Learning Opportunities: Complex projects, rotations, internal tech communities, training, certifications, coaching, online learning platforms subscriptions, pass-it-on sessions, workshops, conferences; Work-Life Balance: Hybrid work and flexible working hours, employee assistance programme; Health: Global internal wellbeing programme, access to wellbeing apps; Community: Global internal tech communities, hobby clubs and interest groups, inclusion and diversity programmes, events and celebrations. At Endava, we’re committed to creating an open, inclusive, and respectful environment where everyone feels safe, valued, and empowered to be their best. We welcome applications from people of all backgrounds, experiences, and perspectives—because we know that inclusive teams help us deliver smarter, more innovative solutions for our customers. Hiring decisions are based on merit, skills, qualifications, and potential. If you need adjustments or support during the recruitment process, please let us know.

Responsibilities

Design, develop, and deploy scalable AI solutions while maintaining AI platform capabilities for rapid experimentation and governance. Collaborate with cross-functional teams to define AI use cases and contribute to MLOps practices.