- Continuously monitor and evaluate emerging developments in artificial intelligence, including advancements in foundational models, generative AI, multi-modal AI, and agentic systems.
- Conduct structured research and experimentation to assess the performance, feasibility, and enterprise applicability of new AI models, frameworks, and architectures.
- Analyze competing technologies and approaches to determine optimal AI solutions and implementation strategies for business use cases.
- Develop research prototypes and proof-of-concepts to validate new AI capabilities before enterprise adoption.
- Provide technical recommendations and research insights to leadership and engineering teams regarding emerging AI opportunities.
- Translate research findings into practical guidance, reference architecture, and implementation frameworks for downstream teams.
- Collaborate with engineering, data science, and product teams to support the adoption of validated AI capabilities.
- Evaluate open-source and commercial AI ecosystems, identifying tools, models, and platforms that can accelerate enterprise AI capabilities.
- Document research findings, benchmarking results, and architectural recommendations to support informed decision-making.
- Mentor junior researchers and contribute to building a strong internal knowledge base around evolving AI technologies.
Key Skills Required:
- Strong understanding of modern AI and machine learning techniques, including large language models, generative AI, and emerging AI architectures.
- Ability to quickly analyze and understand new AI research papers, frameworks, and model releases, and evaluate their practical relevance.
- Experience working with AI ecosystems such as Hugging Face, Ollama, OpenAI, Anthropic, or other foundational model platforms.
- Familiarity with LLM techniques such as prompt engineering, RAG architecture, fine-tuning approaches (LoRA, QLoRA), and model evaluation frameworks.
- Strong logical reasoning and analytical skills to compare different AI approaches and recommend optimal solutions.
- Experience designing and running AI experiments, benchmarking models, and evaluating performance trade-offs.
- Understanding of AI system architecture, model deployment considerations, and integration patterns.
- Strong programming and experimentation skills in Python and common AI frameworks.
- Ability to clearly communicate complex AI concepts and research findings to both technical and non-technical stakeholders.
- Curiosity and passion for staying up to date with the rapidly evolving AI research and innovative landscape.
- Bachelor's degree or advanced degree (Master's or Ph.D.) in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Physics, or a related quantitative discipline
- Minimum 5+ years of professional experience in AI/ML research, development, or applied machine learning roles
- Proven track record of conducting rigorous AI research, including experimentation design, benchmarking, and performance analysis
- Strong analytical and logical reasoning capabilities, with the ability to critically assess competing AI technologies and architectures
- Exceptional written and verbal communication skills, with proven ability to articulate sophisticated AI concepts to both technical and non-technical stakeholders