MLOps Engineer
Role Overview
This is an offshore role responsible for operationalising and maintaining the machine-learning and AI delivery lifecycle for an enterprise Agentic AI solution - pipelines, model and prompt deployment, monitoring, and retraining. The role works across the integration and platform layers that the AI workloads depend on, ensuring they are reliable, observable, and governed across environments within a follow-the-sun delivery model.
Key Responsibilities
Build and operate MLOps pipelines covering model and prompt packaging, deployment, monitoring, and retraining.
Operationalise LLM-based and agentic AI solutions, including prompt orchestration, RAG, embeddings, and vector stores.
Integrate ML/AI pipelines with messaging (Kafka/Confluent, Azure Service Bus) and API gateways (Azure API Management).
Manage model and prompt versioning and promotion across non-production and production, and support batch and real-time inference.
Implement logging, metrics, tracing, and alerting for AI services using Application Insights and Dynatrace, and monitor performance, drift, latency, and availability.
Build CI/CD pipelines for ML and AI artifacts using GitHub Actions and Git-based workflows.
Implement Terraform Infrastructure-as-Code for ML/AI and related platform components.
Collaborate with data scientists and platform engineers, and provide on-call support for AI services.
Qualifications & Experience
4+ years experience in MLOps, AI Engineering, or Platform Engineering.
Strong Python and Linux experience.
Experience with ML lifecycle tooling such as MLflow, Azure ML, or equivalent.
Experience with LLM application patterns including RAG, embeddings, and vector databases.
Experience with containerisation (Docker, Kubernetes), CI/CD, and Terraform Infrastructure as Code.
Exposure to messaging (Kafka or Azure Service Bus) and API gateway integration.
Experience deploying and operating AI systems in production cloud environments, Azure preferred.
Strong communication and collaboration skills.
Preferred Skills
Experience with agentic AI frameworks such as LangGraph.
Experience integrating with Azure OpenAI or Cognitive Services.
Experience with model monitoring and observability tooling (Application Insights, Dynatrace).
Familiarity with API Management and messaging-based integration patterns.
Familiarity with ITIL-based enterprise service management processes.
Role Overview
This is an offshore role responsible for operationalising and maintaining the machine-learning and AI delivery lifecycle for an enterprise Agentic AI solution - pipelines, model and prompt deployment, monitoring, and retraining. The role works across the integration and platform layers that the AI workloads depend on, ensuring they are reliable, observable, and governed across environments within a follow-the-sun delivery model.
Key Responsibilities
Build and operate MLOps pipelines covering model and prompt packaging, deployment, monitoring, and retraining.
Operationalise LLM-based and agentic AI solutions, including prompt orchestration, RAG, embeddings, and vector stores.
Integrate ML/AI pipelines with messaging (Kafka/Confluent, Azure Service Bus) and API gateways (Azure API Management).
Manage model and prompt versioning and promotion across non-production and production, and support batch and real-time inference.
Implement logging, metrics, tracing, and alerting for AI services using Application Insights and Dynatrace, and monitor performance, drift, latency, and availability.
Build CI/CD pipelines for ML and AI artifacts using GitHub Actions and Git-based workflows.
Implement Terraform Infrastructure-as-Code for ML/AI and related platform components.
Collaborate with data scientists and platform engineers, and provide on-call support for AI services.
Qualifications & Experience
4+ years experience in MLOps, AI Engineering, or Platform Engineering.
Strong Python and Linux experience.
Experience with ML lifecycle tooling such as MLflow, Azure ML, or equivalent.
Experience with LLM application patterns including RAG, embeddings, and vector databases.
Experience with containerisation (Docker, Kubernetes), CI/CD, and Terraform Infrastructure as Code.
Exposure to messaging (Kafka or Azure Service Bus) and API gateway integration.
Experience deploying and operating AI systems in production cloud environments, Azure preferred.
Strong communication and collaboration skills.
Preferred Skills
Experience with agentic AI frameworks such as LangGraph.
Experience integrating with Azure OpenAI or Cognitive Services.
Experience with model monitoring and observability tooling (Application Insights, Dynatrace).
Familiarity with API Management and messaging-based integration patterns.
Familiarity with ITIL-based enterprise service management processes.
