Endava
Information Technology And Services
SeniorAIPlatformEngineer
Neural analysis suggests this role is
optimal for mid candidates.
“Senior AI Platform Engineer at Endava. Skills: AI Platform Engineering, MLOps, LLM applications, Cloud-native architectures. Design scalable AI solutions. Develop scalable AI solutions”
Industry & Context.
Pragmatic problem-solving
What They're Looking For.
Must Have
5+ years of experience, programming skills in Python, experience building production-grade backend systems, Hands-on experience with LLM applications, generative AI experience, orchestration frameworks experience, Experience designing RAG architectures, implementing RAG architectures, vector search solutions experience, Solid understanding of cloud-native architectures, public cloud platforms experience, Experience with containers, orchestration technologies experience, MLOps practices familiarity, CI/CD automation familiarity, infrastructure-as-code familiarity, observability tooling familiarity, Experience working with APIs, microservices experience, distributed systems experience, event-driven architectures experience, software engineering best practices understanding, testing understanding, version control understanding, secure development understanding, Advanced English communication skills
Nice to Have
AI governance experience, evaluation frameworks experience, responsible AI practices experience, vector databases knowledge, embedding pipelines knowledge, Experience with workflow automation, orchestration platforms experience, multi-agent systems exposure, autonomous AI workflows exposure, Experience building internal developer platforms, AI enablement tooling experience, Experience in financial services, enterprise-scale environments experience
What You'll Do.
Design scalable AI solutions
Develop scalable AI solutions
Deploy scalable AI solutions
Build AI platform capabilities
Maintain AI platform capabilities
Develop RAG pipelines
Optimize RAG pipelines
Integrate LLM providers
Orchestrate AI services
Create reusable AI services
Create platform components
Implement observability
Collaborate with teams
Define technical requirements
Define delivery roadmaps
Manage model lifecycle
Automate infrastructure
Ensure AI solutions meet standards
Contribute to technical growth
How You'll Work.
Team & Collaboration
cross-functional teams; engineering teams; product teams; data teams; architecture teams; internal engineering teams
Communication Scope
English communication
Process & Methodology
Roadmap planning
Full Job 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. 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 obse
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