[Job - 29203 ] AI Engineering Lead / AI Solutions Architect

At CI&T, we help large enterprises transform the potential of AI into real business impact with AI Deployment, AI-native execution, and tech-integrated business solutions. With 30 years of experience in technological transformation, we accelerate innovation with expertise in Agentic SDLC, Application modernization, Data & AI, Martech and Business strategy. We are 8,000 CI&Ters across more than 25 countries, collaborating to build solutions with real impact. AI is already part of how we work, evolve, and innovate every day. We are looking for an experienced AI Engineering Lead to help our clients identify, design, and implement AI solutions across the Software Development Lifecycle (SDLC) and enterprise business processes. This role requires a combination of technical leadership, AI architecture expertise, hands-on implementation experience, and strong communication skills. The ideal candidate will be able to work directly with clients , evaluate opportunities where AI can create measurable value, define solution architectures, and guide implementation teams from concept through production. Responsibilities AI Strategy & Solution Design Identify opportunities to apply AI across software development, business operations, and enterprise workflows. Evaluate business problems and determine when AI is (or is not) the appropriate solution. Design scalable AI architectures aligned with business goals, security requirements, and operational constraints. Guide clients through AI adoption and implementation strategies. AI Engineering & Architecture Design and implement solutions using Generative AI, LLMs, RAG, agentic workflows, and AI orchestration frameworks. Define architecture patterns for AI-enabled applications and enterprise platforms. Establish best practices for prompt engineering, retrieval strategies, evaluation, and AI governance. Design solutions that balance quality, latency, reliability, and operational cost. SDLC Transformation Drive adoption of AI capabilities across the software development lifecycle. Identify opportunities for AI-assisted development, automated documentation, testing, code review, deployment validation, and engineering productivity improvements. Collaborate with engineering teams to integrate AI into development processes and delivery pipelines. Technical Leadership Lead technical discussions with clients and internal stakeholders. Mentor engineering teams on AI implementation patterns and best practices. Support architecture reviews, solution estimates, and technical decision-making. Contribute to the development of reusable AI accelerators and frameworks. Required Qualifications Strong software engineering background with hands-on experience in Python and modern backend development. Experience designing and deploying AI solutions in production environments. Practical experience with Generative AI, LLMs, RAG architectures, AI agents, and orchestration frameworks. Experience integrating AI capabilities into enterprise applications and workflows. Understanding of AI evaluation, observability, monitoring, and operational best practices. Experience balancing AI quality, latency, scalability, reliability, and cost considerations. Strong architecture and system design skills. Excellent communication skills and ability to engage with technical and non-technical stakeholders. Advanced English proficiency. Preferred Qualifications Experience with cloud platforms such as AWS, Azure, or GCP. Experience with MLOps, CI/CD, and AI deployment pipelines. Experience implementing AI capabilities across the SDLC. Knowledge of vector databases, knowledge retrieval systems, and GraphRAG architectures. Experience leading technical teams or acting as a technical lead or architect. Experience supporting pre-sales activities, solution discovery, or client workshops. What Success Looks Like Identify high-value AI opportunities and translate them into production-ready solutions. Drive measurable improvements in engineering productivity and business outcomes. Establish scalable AI implementation patterns and governance practices. Enable teams and clients to successfully adopt AI across their development and operational processes.