Lead AI/ML Engineer
4 days ago
Armakuni (AWS Premier Partner)
, a trusted partner in helping organizations leverage cloud-native technologies to achieve agility, scalability, and resilience.
- Cloud-Native Transformation :
Guiding businesses through adopting cloud-native practices to optimize their operations - DevOps and Platform Engineering :
Building robust, secure, and scalable platforms tailored to your unique needs. - Data Engineering :
With our data engineering service you can transform your data into actionable business outcome - Gen AI & ML :
Empower Business strategy with AI-Driven growth through Gen-AI Services - Tailored Solutions
: Providing expert consulting services to deliver long-term success for your teams.
Get more insights about us by visiting us on our website
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currently hiring
Lead AI/ML Engineer
to join our team
The following are requirements of skills set:
Skills :
Gen AI, LLMs, AWS Sagemaker, AWS Bedrock, MLOps-Mlflow, Kuberflow
Experience :
5-8 Years
Location :
Ahmedabad
Roles & Responsibilities :
Model Development & Deployment:
- Design, build, and deploy machine learning models to production environments using AWS SageMaker, AWS Bedrock, and other AWS AI/ML services.
- Develop and deploy Generative AI applications using various LLMs.
- Work on data preprocessing, exploratory data analysis (EDA), feature engineering, and model training pipelines using various ML pipelines and AWS services.
- Implement best practices for model versioning, monitoring, and scaling in production.
Collaboration & Communication
- Collaborate with data engineers to translate business requirements into ML models and work on data science-related problems as needed.
- Work with DevOps and Data Engineering teams to deploy and maintain models in production, with a strong understanding of MLOps.
- Present findings and progress to stakeholders, ensuring alignment with business objectives.
Innovation & Improvement
- Continuously explore and implement new AWS AI/ML services (e.g., Amazon Rekognition, Comprehend, Lex, Polly, AWS Bedrock, and open-source LLMs) to improve existing solutions.
- Optimize models for performance, scalability, and cost-effectiveness in the AWS cloud environment.
Documentation & Compliance:
- Maintain detailed documentation of model architectures, training processes, and deployment pipelines.
- Ensure compliance with industry standards and data privacy regulations.
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