Senior AI Engineer - Machine Learning & Cloud Infrastructure - TJ / 1829822

Our client Agay Barho is looking for a Senior AI Engineer \- Machine Learning \& Cloud Infrastructure in Lahore At Agay Barho, the Senior AI Engineer plays a crucial role in designing and developing sophisticated AI and machine learning systems that drive innovation within the company. This position requires deep expertise in Python programming, distributed system architectures, and cloud infrastructure deployment, particularly within AWS environments. The engineer is responsible for building scalable and efficient backend systems, leveraging technologies such as FastAPI and vector databases like Milvus to support advanced retrieval\-augmented generation (RAG) techniques. The role demands a solid understanding of containerization and orchestration tools including Docker, Kubernetes, and Terraform to ensure reliable and repeatable deployments. This position does not involve managing a team but focuses heavily on technical leadership, problem solving, and delivering high\-impact AI solutions that meet business needs. The Senior AI Engineer must have a minimum of five years of professional software engineering experience with strong capabilities in developing AI/ML or large language model (LLM) based systems. The ideal candidate is versed in cloud\-native architectures and can optimize AI pipelines, including prompt engineering for LLMs and agentic AI frameworks. They take ownership of the full software development lifecycle from infrastructure provisioning to deployment and monitoring using tools such as Prometheus, Grafana, and AWS CloudWatch. The engineer collaborates closely with various stakeholders to build enterprise\-scale knowledge systems and improve system performance, reliability, and scalability in cloud environments. This role provides opportunities to work on cutting\-edge AI technologies within a dynamic and innovative organization. **Responsibilities** * Design, develop, and maintain machine learning and large language model\-based applications focused on retrieval\-augmented generation (RAG) architectures. * Implement robust backend APIs using FastAPI and Python to support AI\-driven services and data pipelines. * Deploy applications and infrastructure on AWS, utilizing Amazon EKS, S3, VPC networking, and CloudWatch for monitoring and management. * Build and manage containerized applications with Docker, Kubernetes, and Helm ensuring efficient scaling and orchestration. * Provision and automate cloud resources using Terraform to maintain infrastructure as code and promote best practices. * Integrate and optimize vector databases, preferably Milvus, to enhance AI retrieval efficiency and scalability. * Develop, test, and refine AI models and pipelines, including vector search optimization and embedding processes. * Ensure distributed system architectures are scalable, secure, and reliable to support enterprise\-scale knowledge systems. * Apply prompt engineering and evaluation techniques for large language models to improve AI system responsiveness and accuracy. * Monitor system performance using Prometheus, Grafana, and AWS CloudWatch, proactively identifying and resolving issues. * Collaborate with cross\-functional teams to align technical solutions with business objectives and evolving technology trends. * Stay current with advancements in AI, cloud infrastructure, and software engineering to continuously enhance company technology stacks. * Provide technical expertise on agentic AI frameworks and multi\-agent orchestration to expand AI system capabilities. * Document system designs, architectures, and processes for internal knowledge sharing and future maintenance.