Machine Learning Engineer

Description:

Responsibilities:

• Develop and implement automated pipelines for deploying, monitoring, and updating machine learning models in production environments.

• Design and maintain continuous integration/continuous deployment (CI/CD) pipelines specifically for ML models, ensuring smooth transitions from development to production.

• Monitor model performance in real-time, detecting any degradation or anomalies, and implement automated processes for model retraining and versioning.

• Manage and optimize the infrastructure required for ML workflows, including cloud environments (AWS), containerization (Docker), and orchestration (Kubernetes).

• Work closely with data scientists to understand model requirements, and with engineering teams to ensure the integration of models into production systems.

• Ensure model and data versioning are managed effectively, ensuring the reproducibility of experiments and models.

• Ensure that the ML models are deployed securely and comply with relevant regulatory requirements (e.g., data privacy laws, security standards).

• Document processes, workflows, and best practices for model deployment, monitoring, and maintenance, ensuring knowledge sharing across teams.

Primary Skills:

• Cloud Platforms: AWS: S3, DynamoDB, EC2, VPC, Lambda, CloudWatch etc.

• CI/CD Tools: GitHub Actions

• Container Technologies: Docker, Kubernetes

• Sagemaker

• Scripting Languages: Python, Bash

• Automation: Terraform and Python

• Datadog, CloudWatch

• Devops pipeline creation, deployment

Nice to have :

• Type Script

• Databricks, Glue

Qualifications:

• Bachelors degree with 8+ years of overall experience in building cloud infrastructure services, minimum of 5+ years of experience in designing, building, and maintaining MLOPS pipeline.

• Results-oriented MLOPS professional with a strong background in machine learning, operations, and DevOps. Proven track record of deploying and managing machine learning models at scale. Adept at collaborating with cross-functional teams to deliver efficient and scalable solutions. Proficient in programming languages such as Python, and well-versed in a range of DevOps tools, cloud services, database technologies, generative AI, Langchain, LLM, LLMOPS, and MLOPS.

• Knowledge in all aspects of DevOps (source control, continuous integration, deployments, etc.)

• Knowledge in security implementation best practices on IAM policies, KMS encryption, Secrets Management, Network Security Groups etc.

• Experience working in the SCRUM Environment.