ML Ops

Automating AI/ML model deployment and Setting up monitoring for the ML pipeline Integrate the existing, new codebase to the customer pipelines.

- Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge.

- Providing best practices and executing POC for automated and efficient model operations at scale.

- Querying and working with databases, testing ML models, Git and version control, frameworks like Flask, FastAPI.

- Hands-on experience using large foundation models (e.g. LLMs) and associated tool chains (e.g. langchain) and APIs to build applications, tools and workflows for production.

- Familiarity with experiment tracking frameworks such as MLflow

- Setting up and automating data pipelines using tools such as Airflow, Kafka amd Rabbitmq etc.

- Automating CI/CD pipelines to account for data, code, and model changes

- Good on containerization. Proficiency in tools such as Docker and Kubernetes

- Ability to talk to customer architect team and drive and own the deployment cycle and co-ordinate with AIML developers to deploy the solution

Automating AI/ML model deployment and Setting up monitoring for the ML pipeline Integrate the existing, new codebase to the customer pipelines.

- Programming, working knowledge of machine learning algorithms and frameworks, and domain knowledge.

- Providing best practices and executing POC for automated and efficient model operations at scale.

- Querying and working with databases, testing ML models, Git and version control, frameworks like Flask, FastAPI.

- Hands-on experience using large foundation models (e.g. LLMs) and associated tool chains (e.g. langchain) and APIs to build applications, tools and workflows for production.

- Familiarity with experiment tracking frameworks such as MLflow

- Setting up and automating data pipelines using tools such as Airflow, Kafka amd Rabbitmq etc.

- Automating CI/CD pipelines to account for data, code, and model changes

- Good on containerization. Proficiency in tools such as Docker and Kubernetes

- Ability to talk to customer architect team and drive and own the deployment cycle and co-ordinate with AIML developers to deploy the solution