AI / ML Technical Architect

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.

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currently hiring AI / ML - Tech Lead / Architect to join our team!

The following are requirements of skills set :

Skills : Gen AI, LLMs, AWS Sagemaker, AWS Bedrock, MLOps-Mlflow, Kuberflow

Experience : 5-10 Years

Location : Ahmedabad / Hybrid

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.