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What you will do
- Design, develop, and implement advanced solutions leveraging Large Language Models (LLMs).
- Take full ownership of initiatives, delivering end-to-end solutions with minimal supervision.
- Stay current with the latest advancements in Generative AI, LLMs, RAG systems, and applied research.
- Build and maintain reusable code libraries, tools, and frameworks to accelerate AI development.
- Participate in code reviews to ensure high-quality, maintainable, and scalable solutions.
What you need
Document digitization and OCR methods.
Azure Document Intelligence or equivalent.
Azure Delta Lake.
Behavioral Competencies
Flexible to contribute to ad-hoc initiatives such as PoCs, solution prototyping, and proposal workflows.
Open to working on non-GenAI AI/ML projects (e.g., computer vision, document digitization, data structuring, brainstorming for business use cases).
Proactive in providing timely updates and driving tasks to completion.
Demonstrates responsibility, accountability, curiosity, and an innovative mindset.
Willingness to learn and understand the business context (e.g., Philips domain and data landscape) beyond core technical skills.
If you like wild growth and working with happy, enthusiastic over-achievers, you'll enjoy your career with us!
Not the right fit? Let us know you're interested in a future opportunity by clicking Introduce Yourself in the top-right corner of the page or create an account to set up email alerts as new job postings become available that meet your interest!
Generative AI & NLP
- SaaS-based LLMs: LangChain, LlamaIndex, vector databases, prompt engineering (CoT, ReAct, agents), Azure OpenAI function calling, multimodal models.
- Open-Source and SaaS LLMs: Azure OpenAI, Claude Opus 4.6, GPT-3.5 Turbo, GPT-4, etc.
- At least one agentic Generative AI framework: CrewAI, AutoGen, LangGraph, n8n, LangFlow, SmolAgents, Semantic Kernel.
- Advanced Retrieval-Augmented Generation (RAG) systems: hybrid retrieval, knowledge graph–based retrieval, multi-hop RAG, hierarchical/contextual retrieval strategies, evaluation/monitoring of RAG pipelines.
- Classical NLP: text classification, topic modeling, Q&A systems, conversational AI/chatbots, search, Document AI, summarization, content generation, and Named Entity Recognition (NER).
- Databricks ecosystem: Databricks Genie, Databricks AI/BI, AgentBricks
- MS Copilot Studio and knowledge on no-code/low-code app development.
- MCP server, tools, skills and creation and maintenance of reusable components.
Cloud & DevOps: Azure DevOps, Agile (Azure Boards)
AI/ML Tools: Azure Databricks, MLFlow Model Lifecycle Management, Unity Catalog (Azure Databricks)
Cloud Services: Azure Function Apps, Azure Blob Storage, Azure Cognitive Services, Azure AI Search
AgentOps / LLMOps
Agent monitoring, evaluation, and debugging frameworks.
LLM observability and tracing (LangSmith, LangFuse, Weights & Biases ).
Prompt/version management and experimentation.
Governance, compliance, and cost optimization for LLMs.
CI/CD pipelines in Azure DevOps.
Flask, Docker.
About Fractal Analytics
It's fun to work in a company where people truly BELIEVE in what they are doing!
We're committed to bringing passion and customer focus to the business.
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Source: the Fractal Analytics Workday careers site. We last checked this job 4 h ago. We first saw it 2 days ago. A job on a job board does not always mean that the team interviews people now.
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