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)
Lead data science initiatives for Apple’s US Decision Intelligence team, building predictive models, automated insight pipelines, and GenAI-powered tools to support sales operations.
Responsibilities
Build and scale automated insight pipelines
Develop ML models to detect opportunities and diagnose performance issues
Embed insights into AI agents, dashboards, and GenAI tools
Translate business problems into technical solutions
Present insights to senior leadership
Requirements
Bachelor’s degree in Computer Science, Statistics, Mathematics, Engineering, Economics, Applied Mathematics, Machine Learning, or a related field
Nice to have: GenAI frameworks (LangChain, LlamaIndex, Haystack)
Design, develop, and rigorously test end-to-end Generative AI and agentic AI applications
Build and orchestrate multi-agent systems using frameworks like CrewAI, LangGraph, OpenAI Agents SDK, or Azure AI Agent Framework
Implement retrieval-augmented generation pipelines involving embedding creation, chunking, and vector indexing using FAISS, Pinecone, Chroma, or Milvus
Apply advanced prompting strategies such as Few-Shot Learning, Chain-of-Thought (CoT), ReAct, and CART to improve reliability and interpretability of model responses
Establish automated and manual testing pipelines using TruLens, LangSmith, PromptLayer, or DeepEval to evaluate model quality, accuracy, safety, and factual grounding
