We are seeking a highly skilled Senior LLM Engineer to join our innovative SaaS-based insurance mining platform. We have experienced record growth and are excited to grow the team. The adecuado candidate will have industrial real world production expertise in fine-tuning large language models (LLMs), deep learning, and retrieval-augmented generation (RAG). This role requires hands-on experience with containerized workflows (e.g., Docker), Azure cloud services, and LLM optimization tools such as Unsloth, Lora and Qlora. You will be responsible for building, optimizing, and deploying models with large context windows to drive value for our insurance clients.

This is a fully remote position.

In order to be considered for this role:

- Candidate must have at least 4 years of experience with title ML Engineer or Data Scientist.
- Candidate must have 2 years continuous employment with the same employer
- Candidates must have completed a Stem related degree (not have one in progress)
- Recent grads are NOT a fit for this role.

Key Responsibilities:

- Fine-tune and deploylarge-scale LLMs(e.g., GPT, OPT, Llama, Falcon,Qwen) to extract insights fromstructured and unstructured insurance data(e.g., policy documents, claims data).
- Candidate must have experience with agentic AI with preference on Langchain/Langsmith
- Leveragetransfer learningandparameter-efficient fine-tuning(LoRA, PEFT) to optimize performance for specific tasks, such as document summarization and claims processing.
- Implementlarge-context-window modelsto handle long insurance documents, improving query accuracy in complex data extractions.

2. Retrieval-Augmented Generation (RAG) Systems:





- DevelopRAG pipelinesto enhance LLM performance by integrating with external knowledge sources (e.g.,Pinecone,FAISS,Azure Cognitive Search).
- Implementquery encodingandembedding modelsfor more accurate and contextual RAG queries.
- Optimize embedding-based document search for large insurance databases usingvector databases.
- Design and managecontainerized environmentsusingDockerandDocker Composefor reproducible training and inference workflows.
- Deploy and orchestrate LLMs inAzure Kubernetes Service (AKS)andAzure Machine Learning (AML).
- Implement distributed training workflows for large LLMs usingAzure Databricks,DeepSpeed, orRay.

4. Model Performance & Monitoring:

- Monitor and improve the performance of LLM-based models (e.g., F1-score, perplexity, inference time) with tools such asUnslothfor efficient model evaluation and experimentation.
- Implementerror analysis toolsandautomated retraining pipelinesusingMLOps best practices.
- Optimize for cost-efficiency and scalability in Azure cloud environments.

5. Collaboration & Stakeholder Communication:

- Collaborate withdata engineers,product managers, andbusiness stakeholdersto understand requirements and deliver machine learning solutions tailored to client needs.
- Communicate complex findings and insights effectively through dashboards, reports, and presentations.





Required Qualifications:

Education:

- Bachelor’s or Master’s degree inData Science, Computer Science, Machine Learning, Applied Mathematics, or a related field. A PhD is a plus.

Experience:

- At least two years as data scientist, NLP engineer or MLOPS engineer in last role.
- 4+ years of experience in building and fine-tuning large language models (LLMs).
- Experience withdeep learning frameworks(e.g., PyTorch, TensorFlow) and libraries likeHugging Face Transformers.
- Hands-on experience withretrieval-augmented generation (RAG),vector databases(e.g., Pinecone, Weaviate, FAISS), andsemantic search.
- Strong experience incontainerized workflowsusingDockerandKubernetes.
- Experience withAzure cloud services, includingAzure Machine Learning (AML),Azure Blob Storage, andAKS.

Technical Skills:

- ExpertinPython.
- Familiarity withUnslothor similar model evaluation frameworks for large LLM fine-tuning.
- Strong experience inembedding models(e.g., SentenceTransformers) anddistributed training.
- Knowledge ofMLOps frameworks(e.g., MLflow, Azure Pipelines) for versioning, monitoring, and model retraining.

Preferred Qualifications:

- Experience working withinsurance workflows(e.g., underwriting, claims analysis).
- Familiarity withlong-document models(e.g., Llama2-Long, BigBird, or Memorizing Transformers, Qwen large models).
- Experience withlow-rank fine-tuning (LoRA)andquantization techniquesfor efficient model deployment.
- Knowledge ofAzure OpenAI Servicesfor large-scale NLP applications.

Soft Skills:

- Strong problem-solving and analytical thinking.
- Ability to work indepen

📌 Senior LLM Engineer – (Argentina)
🏢 Lineslip Solutions
📍 Argentina