Associate Engineer-AI Job in Bangalore at Minnesota Mining and Manufacturing Company

3M is hiring for the role of Associate Engineer-AI!

Responsibilities of the candidate:

• Contribute to building full-stack AI applications, including Retrieval-Augmented Generation (RAG), AI agentic systems, and intuitive UI/UX for user-centric experiences.
• Implement AI-driven features using pre-trained models and APIs; apply prompt engineering and integrate services to meet specific business needs.
• Assist in evaluating and fine-tuning large language models (LLMs); run basic benchmarking and model comparisons to balance performance, scalability, and accuracy.
• Support optimization of AI models and pipelines to improve efficiency, reduce latency, and ensure scalability in production environments.
• Collaborate closely with product managers, software engineers, data scientists, and project managers to translate AI capabilities into practical solutions.
• Stay current with advancements in AI technologies, tools, and practices; proactively share learnings with the team.

Requirements

• Bachelor’s degree in computer science, Artificial Intelligence, Data Science, or a related field.
• 2–3 years of experience building full-stack AI applications, including at least 1 year specifically in Generative AI.
• Hands-on experience with AI applications such as RAG solutions and AI agentic systems, plus basic UI/UX implementation.
• Experience evaluating, fine-tuning, and deploying large language models (LLMs) such as GPT, Claude, or similar LLMs.
• Practical skills in prompt engineering and model optimization for cost, scalability, and low-latency performance, including basic benchmarking and model comparisons.
• Excellent communication and collaboration skills with product managers, data scientists, engineers, and stakeholders.
• Familiarity with MLOps practices, including model lifecycle management and CI/CD.
• Experience deploying AI applications on cloud platforms (AWS, Azure, GCP).
• Experience with modern AI development frameworks and tools (e.g., LangChain/LlamaIndex, Hugging Face, OpenAI/Anthropic APIs).
• Exposure to data pipelines, vector databases (e.g., FAISS, Pinecone), and monitoring/evaluation of AI systems.