about the job
As an AI Prototyper & AIOps Engineer, you will rapidly test ideas, build proof-of-concepts, and operationalize AI models across diverse environments. You will work across LLMs, RAG pipelines, multimodal models, forecasting systems, and cloud-native architectures. You will help define standards for model deployment, security, monitoring, and continuous improvement ensuring reliability and scalability.
Key Responsibilities
● Build rapid prototypes using LLMs, RAG, embeddings, and multimodal models.
...
● Design and implement end-to-end AIOps pipelines for training and deployment.
● Stand up cloud infrastructure in GCP/Azure for scalable AI workloads.
● Integrate structured, unstructured, and telemetry-style data into models.
● Implement monitoring, observability, and automated evaluation systems.
● Collaborate with PMs, architects, and engineers to define feasibility.
● Produce technical documentation and contribute to delivery frameworks.
● Experiment with new AI techniques and translate innovation into action.
Minimum Qualifications
● Bachelor’s degree in CS, Engineering, or related.
● 5+ years in ML Engineering, AIOps, or similar applied AI functions.
● Strong Python, Docker, Kubernetes, CI/CD, and cloud experience.
● Familiarity with LLMs, open-source models, vector DBs, and RAG.
● Strong ability to prototype quickly and work with ambiguity.
● Experience integrating data sources at scale
show more
about the job
As an AI Prototyper & AIOps Engineer, you will rapidly test ideas, build proof-of-concepts, and operationalize AI models across diverse environments. You will work across LLMs, RAG pipelines, multimodal models, forecasting systems, and cloud-native architectures. You will help define standards for model deployment, security, monitoring, and continuous improvement ensuring reliability and scalability.
Key Responsibilities
● Build rapid prototypes using LLMs, RAG, embeddings, and multimodal models.
● Design and implement end-to-end AIOps pipelines for training and deployment.
● Stand up cloud infrastructure in GCP/Azure for scalable AI workloads.
● Integrate structured, unstructured, and telemetry-style data into models.
● Implement monitoring, observability, and automated evaluation systems.
● Collaborate with PMs, architects, and engineers to define feasibility.
● Produce technical documentation and contribute to delivery frameworks.
● Experiment with new AI techniques and translate innovation into action.
Minimum Qualifications
● Bachelor’s degree in CS, Engineering, or related.
...
● 5+ years in ML Engineering, AIOps, or similar applied AI functions.
● Strong Python, Docker, Kubernetes, CI/CD, and cloud experience.
● Familiarity with LLMs, open-source models, vector DBs, and RAG.
● Strong ability to prototype quickly and work with ambiguity.
● Experience integrating data sources at scale
show more
