ofi
Website:
ofi.com
Job details:
About us
As a leading provider of high-quality food and beverage ingredients, we work with farming communities across the globe to grow, source and produce ingredients that are good for consumers, farmers, and the world around us. We supply household food brands and manufacturers worldwide with cocoa, coffee, dairy, nuts and spices ingredients which are often grown on our own farms and estates and sourced from hundreds of thousands of farmers across ~50 countries. Along with our diverse manufacturing and innovation capabilities, this means we can provide ingredients for a range of products, from a plant-based latte mix to an almond based snack bar or a dairy-free ice cream. Making a positive impact on people and planet is a core component of our Purpose, to be the change for good food and a healthy future. With a deep-rooted presence in the countries where our ingredients are grown, we are closer to farmers, enabling better quality, and more reliable, traceable, and transparent supply. And whoever we’re with, whatever we’re doing, we always make it real.
Overview of the position
We are looking for an experienced MLOps Engineer to design, build, and maintain scalable machine learning infrastructure on AWS, with a strong focus on DevOps practices, agentic AI system deployment, and end-to-end SDLC ownership. You will bridge the gap between data science, software engineering, and cloud operations — enabling reliable, secure, and automated delivery of ML models and AI agent workflows into production.
Key Responsibilities
MLOps & AI Agentic Systems
• Design and implement CI/CD pipelines for ML models and AI agentic workflows (LLM-based agents, multi-agent orchestration).
• Deploy, monitor, and scale agentic AI applications (e.g., LangChain, AutoGen,, Bedrock Agents) in production environments.
• Build model versioning, experiment tracking, and feature store solutions (e.g., MLflow, SageMaker, Feast).
• Implement observability, logging, and evaluation frameworks for AI agent performance, drift, and hallucination monitoring.
AWS & Cloud Infrastructure
• Architect and manage ML infrastructure using AWS services: SageMaker, Bedrock, Lambda, ECS/EKS, S3, Step Functions, CloudWatch.
• Optimize cost, scalability, and security of cloud-based ML/AI workloads.
• Implement Infrastructure as Code (IaC) using Terraform / CloudFormation / CDK.
DevOps
• Build and maintain robust CI/CD pipelines (Jenkins, GitHub Actions, GitLab CI, AWS CodePipeline).
• Implement containerization and orchestration (Docker, Kubernetes, ECS).
• Manage automated testing, deployment strategies (blue-green, canary), and rollback mechanisms.
• Set up monitoring, alerting, and incident response practices (Prometheus, Grafana, CloudWatch, Datadog).
SDLC & Engineering Practices
• Drive adoption of best practices across the full Software Development Life Cycle — requirements, design, development, testing, deployment, and maintenance.
• Collaborate with data scientists, ML engineers, and product teams to translate requirements into scalable production systems.
• Enforce code quality, version control (Git), peer review, and documentation standards.
• Participate in Agile/Scrum ceremonies and contribute to sprint planning and retrospectives.
Experience and Qualification
. Bachelor's/Master's in Computer Science, Engineering, or related field.
• 3–7 years of experience in MLOps, DevOps, or Cloud Engineering roles.
• Strong hands-on experience with AWS (SageMaker, Lambda, EKS, S3, IAM, Bedrock).
• Experience deploying agentic AI / LLM-based systems in production.
• Proficiency in Python and scripting for automation.
• Strong knowledge of CI/CD tools, Docker, Kubernetes, and IaC (Terraform/CDK).
• Understanding of ML lifecycle: training, deployment, monitoring, retraining.
• Solid grasp of SDLC methodologies (Agile/Scrum/DevOps).
Good to Have
• Experience with LangChain, AutoGen, CrewAI, or similar agent frameworks.
• Knowledge of vector databases (s3vector , OpenSearch).
• Experience with RAG pipelines and prompt engineering.
ofi is an equal opportunity employer and values diversity. All qualified applicants will receive consideration for employment without regard to racial or ethnic origin, color, age, religion or belief, sex, nationality, disability, sexual orientation, gender identity, gender expression, genetic information, or any other characteristic protected by applicable law.
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