Senior Applications Developer

Role Overview

As a DevOps Engineer at Mercer, you will be a key driver in automating and optimizing our software development, deployment, and runtime environments, with a focus on scalable, secure, and reliable AI solutions. You will possess a comprehensive understanding of the Software Development Lifecycle (SDLC), CI/CD pipelines, and infrastructure automation, with an emphasis on containerized workloads and cloud-native architectures. Your role will also include supporting the deployment and management of AI/ML models, ensuring smooth integration of LLMOps/MLOps practices into our digital pipelines.

This position requires a proactive, collaborative engineer who can bridge development, operations, and AI teams to deliver high-quality, scalable AI-enabled applications and services.

We will count on you to:

• Manage and Prioritize Tasks:Self-manage assigned tasks within the DevOps team backlog, ensuring timely delivery and continuous improvement.
• Design & Implement DevOps Processes:Develop, refine, and document CI/CD pipelines, infrastructure-as-code, and automation procedures to streamline software and AI model deployment.
• Collaborate Across Teams:Work closely with development, architecture, and AI/ML teams to understand their deployment requirements, including model serving, data pipelines, and runtime environments.
• Automate Deployment & Infrastructure:Build and maintain automated workflows for software deployment, testing, environment provisioning, and AI/ML model lifecycle management, including versioning, monitoring, and rollback strategies.
• Support AI/ML & LLMOps/MLOps:Implement best practices for deploying, scaling, and monitoring AI models, including large language models, ensuring compliance with security and performance standards.
• Cost & Security Management:Monitor environment costs, ensure environments are patched, secure, and compliant, and optimize resource utilization.
• Maintain Environment Consistency:Ensure environment definitions are version-controlled, reproducible, and aligned with organizational standards.
• Promote Best Practices:Advocate for infrastructure security, reliability, and efficiency, incorporating AI-specific considerations such as model drift detection, data privacy, and model explainability.
• Monitor & Troubleshoot:Use observability tools (DataDog, ElasticSearch, Grafana, etc.) to monitor system health, troubleshoot issues, and optimize AI deployment pipelines.
• Contribute to Innovation:Stay current with emerging DevOps, MLOps, and LLMOps trends, integrating new tools and methodologies to improve deployment workflows.
• Documentation & Knowledge Sharing:Maintain comprehensive documentation of processes, environments, and AI deployment strategies, sharing knowledge across teams.

What You Need to Have:

• Proven experience automating CI/CD pipelines, infrastructure provisioning, and deployment workflows, especially in containerized environments.
• Strong understanding of DevOps best practices, including security, scalability, and cost management.
• Experience working with AI/ML models, including deployment, monitoring, and lifecycle management, with familiarity in LLMOps/MLOps practices.
• Ability to collaborate with AI teams to support model deployment, versioning, and scaling.
• Knowledge of cloud platforms (AWS, Azure, GCP) for deploying AI solutions at scale.
• Familiarity with container orchestration tools such as Kubernetes, Rancher, Helm, and Docker.
• Experience with automation tools like Jenkins, Ansible, GitHub Actions, and scripting languages (Python, Bash).
• Experience with observability and logging tools such as DataDog, ElasticSearch, Logstash, Kibana, and Grafana.
• Understanding of security best practices for cloud and container environments.
• Strong problem-solving skills and a proactive approach to automation and optimization.

What makes you stand out?

• Extensive experience with Docker, building and maintaining images across diverse technologies (MEAN stack, Java, Python, Machine Learning, Kafka, .NET, etc.).
• Proficiency with CI/CD tools such as Jenkins, GitHub Actions, and scripting languages (Python, Bash).
• Hands-on experience with Kubernetes, Rancher, Helm, and container orchestration.
• Familiarity with infrastructure automation tools like Ansible.
• Knowledge of cloud services (AWS, Azure, GCP) for deploying scalable AI/ML workloads.
• Experience with model deployment frameworks and tools supporting MLOps/LLMOps workflows.
• Familiarity with API management (APIGEE) and security tools.
• Experience with monitoring and logging ecosystems (DataDog, ElasticSearch, Logstash, Kibana, Grafana).
• Understanding of software security, environment management, and cost optimization.

Why join our team:

• We help you be your best through professional development opportunities, interesting work and supportive leaders.
• We foster a vibrant and inclusive culture where you can work with talented colleagues to create new solutions and have impact for colleagues, clients and communities.
• Our scale enables us to provide a range of career opportunities, as well as benefits and rewards to enhance your well-being.