Job Description

The AI/ML Engineer will play a key role within the AI Center of Excellence (CoE), focusing on building, scaling, and maintaining robust ML and GenAI operational infrastructure. This position is responsible for developing and automating end-to-end machine learning pipelines, deploying models into production, and ensuring their performance and stability over time. The ideal candidate is a hands-on engineer with strong experience in MLOps, LLMOps, and cloud-native tools, and a passion for reliable, scalable, and efficient AI systems.

ACCOUNTABILITIES: (The primary functions, scope and responsibilities of the role)

Engineering and Operations:

• Develop, deploy, and maintain production-grade ML/GenAI pipelines using AWS cloud-native and open-source MLOps tools.
• Automate model training, evaluation, testing, deployment, and monitoring workflows.
• Implement LLMOps practices for prompt versioning, model tracking, and continuous evaluation of GenAI systems.
• Integrate ML systems with CI/CD pipelines and infrastructure-as-code tools.
• Support model inference at scale via APIs, containers, and microservices.
• Work closely with data engineering to ensure high-quality, real-time, and batch data availability for ML workflows.
• Ensure high availability, reliability, and performance of AI services in production environments.
• Maintain robust monitoring and observability on AWS, Snowflake, SalesForce and Oracle ecosystems.
• Implement feature stores and data versioning systems to ensure reproducible ML experiments and deployments.
• Deploy and optimize vector databases and embedding models for semantic search and RAG applications.
• Configure GPU enabled cloud infrastructure and implement monitoring solutions to optimize resource utilization, costs and performance for ML training and inference workloads.
• Establish automated model validation, testing, and rollback procedures for safe production deployments.

Tooling and Infrastructure:

• Build and manage model registries, feature stores, and metadata tracking systems.
• Leverage containerization (e.g., Docker) and orchestration (e.g, Kubernetes, Airflow, Kubeflow) for scalable deployment. Implement role-based access control, auditing, and governance for ML infrastructure.
• Manage cost-effective cloud infrastructure using AWS.
• Build and maintain data quality monitoring systems with automated alerting for data drift and anomalies.
• Implement cost optimization strategies including auto-scaling, spot instances, and resource right-sizing for ML workloads.

Collaboration and Support:

• Partner with data engineers, data scientists, ML engineers, architects, software engineers, infrastructure and security teams to support scalable and efficient AI/ML workflows.
• Contribute to incident response, performance tuning, and continuous improvement of ML pipelines.
• Provide guidance and documentation to promote reproducibility and best practices across teams.
• Work as part of an agile development team and participate in planning and code reviews.

REQUIRED QUALIFICATIONS: (Minimum qualifications needed for this position including education, experience, certification, knowledge and/or physical requirements)

Knowledge of:

• Cloud-native AI/ML development with AWS.
• MLOps/LLMOps frameworks and lifecycle tools on AWS.
• Monitoring and observability platforms on AWS.
• ML model deployment strategies (e.g., batch, real-time, streaming).
• Feature stores and data versioning tools on AWS and Snowflake
• Model serving frameworks like AWS Sagemaker and AWS Bedrock for scalable inference deployment.
• Vector databases and embedding deployment (e.g., Pinecone, Weaviate, FAISS, pgvector) for LLM and RAG applications.
• LLMOps-specific tools including prompt management platforms and LLM serving optimization on AWS.
• Docker registries and artifact management.

Required Skills and Abilities:

• Strong Python programming and scripting skills.
• Hands-on experience deploying and managing ML/GenAI models in production.
• Experience with Docker, Kubernetes, and workflow orchestration tools like Airflow or Kubeflow.
• Proficiency in infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Ability to debug, troubleshoot, and optimize AI/ML pipelines and systems.
• Comfortable working in agile teams and collaborating cross-functionally.
• Proven ability to automate processes and build reusable ML operational frameworks.
• Experience with A/B testing frameworks and canary deployments for ML models in production environments.
• Knowledge of GPU resource management and optimization for training and inference workloads.
• Understanding data pipeline quality monitoring, drift detection, and automated retraining triggers.
• Experience with secrets management, role-based access control, and secure credential handling for ML systems.

Education and/or Experience:

• Bachelor's degree in computer science, Engineering, or a related field (master's preferred).
• 2-3 years of experience in ML engineering, DevOps, or MLOps roles.
• Demonstrated experience managing production AI/ML workloads and systems.

PREFERRED QUALIFICATIONS: (Additional qualifications that may make a person even more effective in the role, but are not required for consideration)

• Experience with LLMOps and GenAI pipeline monitoring.
• Cloud certifications in AWS, Azure, or GCP.
• Experience supporting AI applications in regulated industries (e.g., healthcare, finance).
• Contributions to open-source MLOps tools or infrastructure projects.
• Experience with edge deployment and model optimization techniques (quantization, pruning, distillation).
• Knowledge of compliance frameworks (SOC2, GDPR, HIPAA) and security best practices for AI/ML systems.
• Experience with real-time streaming data pipelines (Kafka, Kinesis) and event-driven ML Architectures.

Responsibilities

The AI/ML Engineer will play a key role within the AI Center of Excellence (CoE), focusing on building, scaling, and maintaining robust ML and GenAI operational infrastructure. This position is responsible for developing and automating end-to-end machine learning pipelines, deploying models into production, and ensuring their performance and stability over time. The ideal candidate is a hands-on engineer with strong experience in MLOps, LLMOps, and cloud-native tools, and a passion for reliable, scalable, and efficient AI systems.

ACCOUNTABILITIES: (The primary functions, scope and responsibilities of the role)

Engineering and Operations:

• Develop, deploy, and maintain production-grade ML/GenAI pipelines using AWS cloud-native and open-source MLOps tools.
• Automate model training, evaluation, testing, deployment, and monitoring workflows.
• Implement LLMOps practices for prompt versioning, model tracking, and continuous evaluation of GenAI systems.
• Integrate ML systems with CI/CD pipelines and infrastructure-as-code tools.
• Support model inference at scale via APIs, containers, and microservices.
• Work closely with data engineering to ensure high-quality, real-time, and batch data availability for ML workflows.
• Ensure high availability, reliability, and performance of AI services in production environments.
• Maintain robust monitoring and observability on AWS, Snowflake, SalesForce and Oracle ecosystems.
• Implement feature stores and data versioning systems to ensure reproducible ML experiments and deployments.
• Deploy and optimize vector databases and embedding models for semantic search and RAG applications.
• Configure GPU enabled cloud infrastructure and implement monitoring solutions to optimize resource utilization, costs and performance for ML training and inference workloads.
• Establish automated model validation, testing, and rollback procedures for safe production deployments.

Tooling and Infrastructure:

• Build and manage model registries, feature stores, and metadata tracking systems.
• Leverage containerization (e.g., Docker) and orchestration (e.g, Kubernetes, Airflow, Kubeflow) for scalable deployment. Implement role-based access control, auditing, and governance for ML infrastructure.
• Manage cost-effective cloud infrastructure using AWS.
• Build and maintain data quality monitoring systems with automated alerting for data drift and anomalies.
• Implement cost optimization strategies including auto-scaling, spot instances, and resource right-sizing for ML workloads.

Collaboration and Support:

• Partner with data engineers, data scientists, ML engineers, architects, software engineers, infrastructure and security teams to support scalable and efficient AI/ML workflows.
• Contribute to incident response, performance tuning, and continuous improvement of ML pipelines.
• Provide guidance and documentation to promote reproducibility and best practices across teams.
• Work as part of an agile development team and participate in planning and code reviews.

REQUIRED QUALIFICATIONS: (Minimum qualifications needed for this position including education, experience, certification, knowledge and/or physical requirements)

Knowledge of:

• Cloud-native AI/ML development with AWS.
• MLOps/LLMOps frameworks and lifecycle tools on AWS.
• Monitoring and observability platforms on AWS.
• ML model deployment strategies (e.g., batch, real-time, streaming).
• Feature stores and data versioning tools on AWS and Snowflake
• Model serving frameworks like AWS Sagemaker and AWS Bedrock for scalable inference deployment.
• Vector databases and embedding deployment (e.g., Pinecone, Weaviate, FAISS, pgvector) for LLM and RAG applications.
• LLMOps-specific tools including prompt management platforms and LLM serving optimization on AWS.
• Docker registries and artifact management.

Required Skills and Abilities:

• Strong Python programming and scripting skills.
• Hands-on experience deploying and managing ML/GenAI models in production.
• Experience with Docker, Kubernetes, and workflow orchestration tools like Airflow or Kubeflow.
• Proficiency in infrastructure-as-code tools (e.g., Terraform, CloudFormation).
• Ability to debug, troubleshoot, and optimize AI/ML pipelines and systems.
• Comfortable working in agile teams and collaborating cross-functionally.
• Proven ability to automate processes and build reusable ML operational frameworks.
• Experience with A/B testing frameworks and canary deployments for ML models in production environments.
• Knowledge of GPU resource management and optimization for training and inference workloads.
• Understanding data pipeline quality monitoring, drift detection, and automated retraining triggers.
• Experience with secrets management, role-based access control, and secure credential handling for ML systems.

Education and/or Experience:

• Bachelor's degree in computer science, Engineering, or a related field (master's preferred).
• 2-3 years of experience in ML engineering, DevOps, or MLOps roles.
• Demonstrated experience managing production AI/ML workloads and systems.

PREFERRED QUALIFICATIONS: (Additional qualifications that may make a person even more effective in the role, but are not required for consideration)

• Experience with LLMOps and GenAI pipeline monitoring.
• Cloud certifications in AWS, Azure, or GCP.
• Experience supporting AI applications in regulated industries (e.g., healthcare, finance).
• Contributions to open-source MLOps tools or infrastructure projects.
• Experience with edge deployment and model optimization techniques (quantization, pruning, distillation).
• Knowledge of compliance frameworks (SOC2, GDPR, HIPAA) and security best practices for AI/ML systems.
• Experience with real-time streaming data pipelines (Kafka, Kinesis) and event-driven ML Architectures.