AI Engineer

Shashwath Solution; Dadri; On-site; INR 1,200,000 - 1,800,000; Full time

Job summary

Shashwath Solution is seeking a cloud-native ML engineer to design, build, and deploy end-to-end ML pipelines in scalable cloud environments in Dadri.

You will work with data scientists to productionalize models, optimize architectures on AWS, Azure, and GCP, and implement MLOps with CI/CD, monitoring, and retraining workflows. Strong Python and SQL skills with Kubernetes and Docker are essential.

Qualifications

• 4+ years of experience in cloud-native ML engineering with scalable AI model deployment.
• Hands-on with Azure ML, Google AI Platform, AWS SageMaker and cloud-native services.
• Strong Python and SQL, with Kubernetes and Docker expertise. Proficient in Linux.
• Experience operationalizing GenAI applications and ML pipelines with monitoring.

Responsibilities

• Design, develop, and deploy end-to-end ML pipelines in cloud-native environments.
• Collaborate with data scientists to productionalize models from prototype to enterprise-ready.
• Optimize cloud data architectures and ML systems for performance and cost.
• Integrate ML models into architectures and design robust APIs for consumption.
• Implement MLOps/LLMOps with CI/CD, monitoring, and retraining workflows.
• Continuously assess ML system performance and ensure high availability.
• Stay updated on AI and cloud trends, partnering with cloud architects on new tech.

Skills

kubernetes

docker

microsoft azure

gcp

aws sagemaker

azure devops

linux

google

ai platform

machine learning

ml

artificial intelligence

python

sql

linux internals

cloud infra mgmt

aws

Job description

Responsibilities

• Design, develop, and deploy end-to-end machine learning pipelines in cloud-native environments, ensuring scalability and reliability.
• Collaborate with data scientists to productionalize ML models, transitioning from prototype to enterprise-ready solutions.
• Optimize cloud-based data architectures and ML systems for high performance and cost efficiency (AWS, Azure, GCP).
• Integrate ML models into existing and new system architectures, designing robust APIs for seamless model consumption.
• Implement MLOps and LLMOps best practices, including CI/CD for ML models, monitoring, and automated retraining workflows.
• Continuously assess and improve ML system performance, ensuring high availability and minimal downtime.
• Stay ahead of AI and cloud trends, collaborating with cloud architects to leverage cutting-edge technologies

Qualifications

• 4+ years of experience in cloud-native ML engineering, deploying and maintaining AI models at scale.
• Hands-on experience with AI cloud platforms (Azure ML, Google AI Platform, AWS SageMaker) and cloud-native services.
• Strong programming skills in Python and SQL, with expertise in cloud-native tools like Kubernetes and Docker.
• Experience building automated ML pipelines, including data preprocessing, model deployment, and monitoring.Proficiency in Linux environments and cloud infrastructure management.Experience operationalizing GenAI applications or AI assistants is a plus.Strong problem-solving, organizational, and communication skills.

Mandatory Key Skills

• kubernetes
• docker
• microsoft azure
• gcp
• aws sagemaker
• azure devops
• linux
• google
• ai platform
• machine learning
• ml
• artificial intelligence
• java
• natural language processing
• python*
• sql*
• linux internals*
• cloud infrastructure management*
• aws*