Applied AI Scientist

Applied AI Scientist – Job Description

Job Title

Applied AI Scientist

Location

[City/Remote/Hybrid]

Employment Type

Full-time / Contract

Job Summary

We are seeking an Applied AI Scientist to research, design, develop, and deploy AI solutions that address real-world business challenges. The ideal candidate combines expertise in machine learning, deep learning, natural language processing (NLP), computer vision, and generative AI with strong problem-solving and software engineering skills. This role involves translating research into production-ready AI applications, collaborating with cross-functional teams, and driving innovation across AI initiatives.

Key Responsibilities

• Research, design, and develop AI and machine learning solutions for business and product use cases.

• Build, train, fine-tune, evaluate, and optimize machine learning and deep learning models.

• Develop applications using large language models (LLMs), multimodal AI, and generative AI technologies.

• Design and implement Retrieval-Augmented Generation (RAG), AI agents, and intelligent automation solutions.

• Conduct experiments to evaluate model accuracy, robustness, scalability, and business impact.

• Analyze structured and unstructured data to derive insights and improve model performance.

• Collaborate with data scientists, AI engineers, software developers, product managers, and business stakeholders.

• Translate research findings into scalable, production-ready AI systems.

• Implement model monitoring, evaluation, and continuous improvement processes.

• Publish technical documentation, research findings, and reusable AI assets where appropriate.

• Stay current with advancements in AI, foundation models, reinforcement learning, and emerging technologies.

Required Qualifications

• Master's or Ph.D. in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Mathematics, Statistics, or a related field.

• 3–8+ years of experience in AI research, applied machine learning, or data science.

• Strong knowledge of machine learning, deep learning, NLP, computer vision, and statistical modeling.

• Experience developing and deploying production AI applications.

• Proficiency in Python and experience with AI/ML frameworks such as PyTorch, TensorFlow, and Scikit-learn.

• Hands-on experience with LLMs, prompt engineering, embeddings, vector databases, and RAG architectures.

• Strong understanding of experimental design, model evaluation, and performance optimization.

• Excellent analytical, communication, and problem-solving skills.

Preferred Qualifications

• Experience with multimodal AI, reinforcement learning, or agentic AI systems.

• Familiarity with distributed training and large-scale model deployment.

• Experience with cloud AI platforms and MLOps practices.

• Publications, patents, or contributions to open-source AI projects.

• Experience in industries such as healthcare, finance, manufacturing, retail, or telecommunications.

• Professional certifications in AI, machine learning, or cloud technologies.

Technical Skills

• Python

• SQL

• Machine Learning

• Deep Learning

• Natural Language Processing (NLP)

• Computer Vision

• Large Language Models (LLMs)

• Generative AI

• Prompt Engineering

• Retrieval-Augmented Generation (RAG)

• AI Agents

• Reinforcement Learning (preferred)

• PyTorch

• TensorFlow

• Scikit-learn

• Hugging Face Transformers

• LangChain

• LlamaIndex

• Vector Databases (Pinecone, Weaviate, Chroma, FAISS, Milvus)

• OpenAI API

• Google Gemini API

• Anthropic API

• FastAPI

• Docker

• Kubernetes

• Git

• MLflow

• REST APIs

• AWS, Microsoft Azure, or Google Cloud

Soft Skills

• Research and analytical thinking

• Problem-solving

• Innovation and creativity

• Communication and presentation

• Cross-functional collaboration

• Critical thinking

• Project management

• Adaptability

• Continuous learning

Key Deliverables

• AI models and production-ready AI applications

• Research prototypes and proof of concepts (POCs)

• Model evaluation and benchmarking reports

• AI solution architectures

• Technical documentation

• Experimentation reports

• Reusable AI components and frameworks

• Business impact assessments

Success Metrics

• Model accuracy, precision, recall, and other performance metrics

• Successful deployment of AI solutions into production

• Business impact and measurable value delivered

• Scalability, reliability, and efficiency of AI systems

• Innovation through research contributions and new AI capabilities

• Reduction in model inference latency and operational costs

• Stakeholder satisfaction and adoption of AI solutions

• On-time delivery of AI research and development milestones