Description :

As a Senior MLOps Engineer within the AI Platform Engineering team, you will help design, build, and scale the foundational capabilities that power AI across our enterprise.

Youll work at the intersection of machine learning, software engineering, and DevOps, enabling AI delivery teams to reliably develop, deploy, and monitor models in production.

You will :

- Design and implement robust, scalable MLOps pipelines to automate the end-to-end machine learning lifecycle from training to deployment to monitoring.

- Collaborate with data scientists, ML engineers, and platform engineers to operationalize AI models across cloud and hybrid environments, ensuring performance, traceability, and reliability.

- Build and maintain containerized training and inference environments, enabling reproducible ML workflows across projects and teams.

- Develop and optimize model CI/CD workflows using tools like GitHub Actions or Jenkins.

- Establish and monitor model observability metrics: latency, drift, data quality, and inference errors, across production pipelines.

- Support the integration of ML models into a variety of deployment targets, including internal applications, APIs, business dashboards, and hardware platforms onboard agricultural machinery.

- Collaborate closely with enterprise architecture, BI engineering, and product engineering teams to ensure seamless deployment across cloud, edge, and embedded systems.

- Serve as a technical mentor and contributor, helping define best practices around model deployment, monitoring, versioning, and rollback strategies.

- Partner closely with AI delivery and analytics teams to drive adoption of the AI platforms tools and services, incorporating their feedback into roadmap improvements.

- Continuously explore and evaluate emerging tools in the MLOps ecosystem, recommending innovative ways to increase velocity, visibility, and scalability.

Your Experience & Qualifications :

Must-Have :

- 8+ years of experience in software, data, or ML engineering, including 5+ years in MLOps or operationalizing AI/ML workflows

- Deep hands-on experience with Python, Spark/PySpark, SQL, and orchestration tools such as Airflow

- Proven experience with public cloud platforms, preferably GCP (Vertex AI, GKE, Cloud Run) or AWS

- Skilled in Databricks, FastAPI, and containerized environments using Docker, Kubernetes

- Deep understanding of CI/CD pipelines, version control systems (e.g., Git), and infrastructure-as-code (Terraform)

- Expertise with MLOps tools like MLflow, Kubeflow, or similar for model tracking, versioning, deployment, and monitoring

- Proficient with model monitoring frameworks (e.g., Prometheus, Grafana) and implementing model drift, performance degradation, and rollback mechanisms

- Familiarity with BI integration tools (e.g., Tableau) for exposing model outputs to business users

- Demonstrated ability to contribute to roadmap execution, and deliver outcomes

- Self-motivated, proactive, and comfortable working both independently and collaboratively across engineering, data science, and business teams

- Strong communication and stakeholder engagement skills, with a focus on impact and execution

Nice-to-Have :

- Practical experience with both structured and unstructured data pipelines supporting model training and real-time inference

- Familiarity with modern ML and deep learning frameworks, including Scikit-learn, TensorFlow, PyTorch

- Experience in utilization of foundation models (e.g., GPT, BERT, CLIP, DINO, SAM)

- Exposure to feature stores, unstructured data databases, or retrieval-augmented generation (RAG) pipelines

- Experience in agriculture, manufacturing, or supply chain domains with real-world ML applications