Senior AI & MLOps Engineer

Accountabilities:: Deploy, configure, and manage Agentic AI and Generative AI solutions across AWS, Azure, and Google Cloud environments, including private and hybrid cloud infrastructures. Design and implement scalable, secure, and resilient cloud architectures tailored to enterprise AI applications. Build, maintain, and optimize CI/CD pipelines, containerization strategies, and automated deployment processes. Establish and implement MLOps and LLMOps practices to streamline model development, deployment, monitoring, and lifecycle management. Implement AI monitoring, observability, and performance optimization strategies to ensure production reliability and operational efficiency. Integrate AI platforms with enterprise applications, APIs, databases, authentication systems, and existing technology ecosystems. Collaborate directly with client cloud architects, infrastructure teams, and security stakeholders to coordinate deployments and resolve technical challenges. Take ownership of end-to-end technical implementations, managing the transition from proof of concept through production deployment and go-live. Apply best practices in cloud security, access management, networking, and secrets management to protect enterprise AI environments. Work closely with internal AI product engineering teams to enhance deployment processes, improve scalability, and increase solution reliability. Lead technical discussions with clients, communicate architectural decisions, and provide recommendations to address complex implementation requirements. Continuously evaluate emerging AI, cloud, and MLOps technologies to improve deployment strategies and operational capabilities. Requirements: 7–12 years of overall IT experience, with a strong background in enterprise technology and software engineering. At least 4 years of hands-on experience in Cloud Engineering, DevOps, or MLOps. A minimum of 2 years of practical experience deploying or integrating AI and Generative AI applications. Proven experience deploying and managing enterprise applications in production environments. Strong knowledge of Large Language Models (LLMs), Agentic AI, Retrieval-Augmented Generation (RAG), and Generative AI architectures. Proficiency in Python and hands-on experience with AI development frameworks such as LangChain and LangGraph. Strong practical expertise in at least two major cloud platforms: AWS, Microsoft Azure, and Google Cloud Platform (GCP). Experience with DevOps and infrastructure automation technologies, including Docker, Kubernetes, Terraform, Helm, and CI/CD pipelines. Knowledge of MLOps and LLMOps tools and platforms such as MLflow, Vertex AI, Azure Machine Learning, Amazon SageMaker, or equivalent solutions. Experience implementing monitoring and observability solutions using Prometheus, Grafana, OpenTelemetry, or comparable technologies. Strong understanding of cloud security principles, including Identity and Access Management (IAM), Role-Based Access Control (RBAC), OAuth, cloud networking, and secrets management. Ability to independently manage technical discussions with clients, coordinate cross-functional teams, and resolve complex deployment challenges. Excellent communication, stakeholder management, analytical thinking, and problem-solving skills. Strong sense of ownership, adaptability, and ability to work independently in a fast-paced, client-facing environment. Preferred Qualifications: Experience deploying Agentic AI products and autonomous AI solutions within enterprise environments. Previous experience with multi-cloud architectures, private cloud infrastructures, and hybrid cloud deployments. Relevant certifications in AWS, Microsoft Azure, or Google Cloud. Experience designing and optimizing production-grade AI systems with a focus on scalability, security, and operational reliability. Benefits: Remote work: Fully remote position, offering flexibility to work from your preferred location in United States. Full-time opportunity: Join a technology-focused environment working on enterprise-scale AI and cloud engineering initiatives. Cutting-edge technologies: Gain hands-on exposure to Agentic AI, Generative AI, LLMs, cloud platforms, and modern MLOps/LLMOps technologies. Technical ownership: Lead end-to-end AI implementations and influence cloud architecture, deployment strategies, and production operations. Professional growth: Expand your expertise across multiple cloud platforms and emerging AI technologies. Client-facing experience: Collaborate directly with enterprise architects, infrastructure specialists, security teams, and business stakeholders. Innovation-driven environment: Contribute to the development and deployment of advanced AI-powered products with tangible enterprise impact.