Sr. AI ML/Ops Engineer

Roles and Responsibilities

1. MLOps Strategy & Implementation

• Design and implement scalable MLOps pipelines for the end-to-end lifecycle of machine learning models (from data ingestion to model deployment and monitoring).
• Automate model training, testing, validation, and deployment using CI/CD practices.
• Collaborate with data scientists to productize ML models.

2. Infrastructure Management

• Build and maintain cloud-native infrastructure (e.g., AWS/GCP/Azure) for training, deploying, and monitoring ML models.
• Optimize compute and storage resources for ML workloads.
• Containerize ML applications using Docker and orchestrate them with Kubernetes.

3. Model Monitoring & Governance

• Set up monitoring for ML model performance (drift detection, accuracy drop, latency).
• Ensure compliance with ML governance policies, versioning, and auditing.

4. Collaboration & Communication

• Work with cross-functional teams (Data Engineering, DevOps, and Product) to ensure smooth ML model deployment and maintenance.
• Provide mentorship and technical guidance to junior engineers.

5. Automation & Optimization

• Automate feature extraction, model retraining, and deployment processes.
• Improve latency, throughput, and efficiency of deployed models in production.

Technical Skills / Tech Stack

1. Programming Languages

• Python

  (primary for ML/AI and scripting)

• Bash/Shell

• Go

  or

  Java

  (optional but valuable for performance-critical components)

2. ML Frameworks & Libraries

• TensorFlow

  ,

  PyTorch

  ,

  Scikit-learn

• MLflow

  ,

  Kubeflow

  , or

  SageMaker

• ONNX

  (for model conversion)

3. Data & Pipeline Tools

• Apache Airflow

  ,

  Luigi

• Kafka

  ,

  Apache Beam

  ,

  Spark

  (for streaming/batch data)

• Pandas

  ,

  Dask

  ,

  NumPy

4. DevOps & MLOps Tools

• Docker

  ,

  Kubernetes

  ,

  Helm

• Terraform

  ,

  Pulumi

  (for infrastructure as code)

• Jenkins

  ,

  GitHub Actions

  ,

  Argo Workflows

• MLflow

  ,

  DVC

  ,

  Tecton

  ,

  Feast

5. Cloud Platforms

• AWS

  : S3, EKS, SageMaker, Lambda, CloudWatch

• GCP

  : GKE, AI Platform, BigQuery, Dataflow

• Azure

  : Azure ML, AKS, Blob Storage

6. Monitoring & Logging

• Prometheus

  ,

  Grafana

• ELK Stack

  ,

  Datadog

  ,

  Cloud-native monitoring tools

7. CI/CD & Versioning

• Git

  ,

  GitOps

  ,

  CI/CD pipelines

  for model and data versioning

Preferred Experience

• 5+ years in AI/ML engineering roles.
• Experience building MLOps pipelines in production.
• Familiarity with regulatory and ethical considerations in ML (e.g., fairness, bias detection, explainability).
• Strong debugging and performance tuning skills in distributed environments.