We are seeking an experienced Cloud AI/MLOps Engineer to design, build, and maintain scalable AI/ML systems in cloud environments. This role combines data engineering, machine learning operations, and cloud infrastructure expertise to deliver productionready AI solutions. The ideal candidate will have 49 years of experience in ML engineering, data pipelines, and cloud platforms with handson experience in generative AI technologies.
Responsibilities
Design, implement, and maintain endtoend ML pipelines for model training, validation, and deployment Develop and optimize MLOps workflows using tools like MLflow, Kubeflow, or SageMaker Pipelines Implement model versioning, experiment tracking, and automated model retraining systems Monitor model performance, data drift, and implement automated alerting systems Collaborate with data scientists to productionize ML models and ensure scalability Design, implement, and maintain endtoend ML pipelines for model training, validation, and deployment Develop and optimize MLOps workflows using tools like MLflow, Kubeflow, or SageMaker Pipelines Implement model versioning, experiment tracking, and automated model retraining systems Monitor model performance, data drift, and implement automated alerting systems Design and implement scalable ML infrastructure on AWS using services like SageMaker, EC2, EKS, Lambda Manage data storage and processing using S3, RDS, Redshift, and EMR Implement autoscaling solutions for ML workloads and cost optimization strategies Configure VPC, security groups, IAM roles, and implement cloud security best practices Deploy containerized applications using AWS ECS/EKS and manage serverless architectures
Build and maintain CI/CD pipelines for ML model deployment using Jenkins, GitLab CI, or AWS CodePipeline Implement Infrastructure as Code (IaC) using Terraform, CloudFormation, or AWS CDK Containerize ML applications using Docker and orchestrate with Kubernetes Automate testing frameworks for ML models including unit tests, integration tests, and model validation Implement monitoring and logging solutions using CloudWatch, Prometheus, or ELK stack
Mandatory skill sets
Programming Languages Proficiency in Python, with experience in SQL and bash scripting ML Frameworks Handson experience with TensorFlow, PyTorch, Pyspark, scikitlearn, and Hugging Face Transformers Cloud Platforms 3+ years of AWS experience with MLspecific services (SageMaker, Bedrock, Comprehend), lambda, ECS/EKS MLOps Tools Experience with MLflow, Kubeflow, DVC, or similar model management platforms DevOps Tools Proficiency in Docker, Kubernetes, Terraform, Jenkins, Git, and CI/CD practices Data Engineering Fundamentals ETL/ELT Pipelines, Data Warehousing Concepts, Apache Spark (Basic), Data Quality & Validation Basic Generative AI LLM APIs (OpenAI/AWS Bedrock), Basic Prompt Engineering, Vector Databases.
Preferred skill sets
Programming & Languages Java/Scala, Go/Rust, JavaScript/TypeScript, R Advanced ML/AI Hugging Face Transformers, XGBoost/LightGBM, Apache Spark MLlib, ONNX, TensorFlow Extended (TFX) MultiCloud & Advanced Infrastructure Azure ML/Google Cloud AI, Edge Computing, Advanced Serverless, Cloud Cost Optimization
MLOps & Orchestration Tools MLflow, Kubeflow, DVC, Weights & Biases, Apache Airflow DevOps & Infrastructure Helm Charts, Istio/Service Mesh, Prometheus/Grafana, ELK Stack, HashiCorp Vault AI/ML Specializations Computer Vision (OpenCV), NLP, Time Series Analysis, Reinforcement Learning, AutoML Advanced Generative AI LLM Finetuning, RAG Systems, LangChain/LlamaIndex, Vector Search Optimization, Multimodal AI Data Engineering Apache Kafka, Apache Spark (Advanced), Databricks, Snowflake, dbt, AWS EMR/Glue, Stream Processing, Data Mesh Architecture Security & Compliance ML Security, Data Privacy (GDPR/HIPAA), Model Goverce, Federated Learning
Years of experience required
49 Years
Education qualification
B. E/ B. Tech
Education
Degrees/Field of Study required MBA (Master of Business Administration), Bachelor of Engineering
Degrees/Field of Study preferred
Required Skills
Microsoft Azure
Accepting Feedback, Accepting Feedback, Active Listening, Analytical Thinking, Business Case Development, Business Data Analytics, Business Intelligence and Reporting Tools (BIRT), Business Intelligence Development Studio, Communication, Competitive Advantage, Continuous Process Improvement, Creativity, Data Analysis and Interpretation, Data Architecture, Database Management System (DBMS), Data Collection, Data Pipeline, Data Quality, Data Science, Data Visualization, Embracing Change, Emotional Regulation, Empathy, Inclusion, Industry Trend Analysis
