Partner with technical sales teams as a subject matter expert in Generative AI to differentiate Google Cloud to customers, develop solutions, and troubleshoot technical issues.
Locations (4)
São Paulo, São Paulo, Brazil
Santiago, Santiago, Chile
Bogotá, Bogota, Colombia
Mexico City, CDMX, Mexico
Responsibilities
Partner with the business team to identify and qualify AI business opportunities
Act as the primary technical advisor for AI adoption
Support bid responses, product and solution briefings
Demonstrate and prototype integrations in customer and partner environments
We are looking for an AI Consultant to help the largest global enterprises get real business value from Artificial Intelligence on AWS, as part of AWS ProServe India LLP.
Responsibilities
Understand the internal customer’s business need and guide them to a solution using our AWS AI Services, AWS AI Platforms, AWS AI Frameworks, and AWS AI EC2 Instances
Assist internal customers by delivering a ML/DL project from beginning to end, including understanding the business need, aggregating data, exploring data, building & validating predictive models, and deploying completed models to deliver business impact to the organization
Use Deep Learning frameworks like MXNet, Caffe 2, Tensorflow, Theano, CNTK, and Keras to help our internal customers build DL models
Use SparkML and Amazon Machine Learning (AML) to help our internal customers build ML models
Work with our Professional Services Big Data consultants to analyze, extract, normalize, and label relevant data
Requirements
Nice to have: customer-facing consulting roles
Nice to have: turning business problems into technical solutions
Nice to have: presenting to technical and business stakeholders
Nice to have: enabling partner delivery teams
Nice to have: AWS certifications such as AWS Certified Machine Learning – Specialty, AWS Certified AI Practitioner or AWS Certified Solutions Architect
Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.
Responsibilities
Perform independently and become an SME
Active participation/contribution in team discussions
Contribute in providing solutions to work related problems
Collaborate with cross-functional teams to understand project requirements and translate them into technical solutions
Maintain and enhance existing software components to improve performance and reliability
Design, evaluate, optimize, and operationalize enterprise AI systems across the complete model lifecycle.
Compensation
Medtronic offers a competitive Salary and flexible Benefits Package
Responsibilities
Design and implement training, fine-tuning, and adaptation pipelines for open-weight Large Language Models using supervised fine-tuning, LoRA, QLoRA, and Parameter-Efficient Fine-Tuning (PEFT) techniques.
Build and curate high-quality datasets from operational data sources, incidents, runbooks, logs, knowledge articles, and validated interactions for model training and evaluation.
Develop model evaluation frameworks covering reasoning, instruction following, hallucination detection, tool calling, safety, robustness, and structured output quality.
Evaluate and optimize retrieval-augmented generation systems through retrieval analysis, embedding model comparison, reranking, chunking strategies, and groundedness assessment.
Design and execute agent and tool-calling benchmarks that measure task completion, tool selection accuracy, failure recovery, and operational safety.
Requirements
Bachelor's Degree in Engineering
MCA
MSc
Nice to have: direct preference optimization (DPO)
As an IT Developer focused on Applied AI and Large Language Model engineering, you will design, evaluate, optimize, and operationalize enterprise AI systems across the complete model lifecycle.
Compensation
Medtronic offers a competitive Salary and flexible Benefits Package
Responsibilities
Design and implement training, fine-tuning, and adaptation pipelines for open-weight Large Language Models using supervised fine-tuning, LoRA, QLoRA, and Parameter-Efficient Fine-Tuning (PEFT) techniques.
Build and curate high-quality datasets from operational data sources, incidents, runbooks, logs, knowledge articles, and validated interactions for model training and evaluation.
Develop model evaluation frameworks covering reasoning, instruction following, hallucination detection, tool calling, safety, robustness, and structured output quality.
Evaluate and optimize retrieval-augmented generation systems through retrieval analysis, embedding model comparison, reranking, chunking strategies, and groundedness assessment.
Design and execute agent and tool-calling benchmarks that measure task completion, tool selection accuracy, failure recovery, and operational safety.
Requirements
Bachelor's Degree in Engineering
MCA
MSc
Nice to have: direct preference optimization (DPO)
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