**Job Title: LLMOPS Data Scientist**
Location: Mexico or LATAM countries - 100% remote
Time zone: early EST
**What we need**:
**What we need**:
- LLMOps
- RAG/Langchain
- GenAI
"The project scope has increased from building MVP to enterprise grade platform and the LLMOps Scientists will be responsible for scaling, new feature additions, enhancements, and onboarding of models. The addition of the AI Project Manager is a net new add to bring aboard more AI specific knowledge to the services being currently provided. The LLMOps Engineering service and an additional one (1) Senior Technical Program Manager is being added in now that we have released classical ML monitoring platform (Phase 1), for Phase 2 the team is extending it to LLM, so our service will need LLMOps expertise and additional Technical Program Management to complete this work."
The client is seeking a talented and experienced **LLMOPS Data Scientist** to join our growing team.
As a member of our Data Science group, you will play a crucial role in developing and deploying state-of-the-art language models and ensuring their responsible and efficient operation.
If you're passionate about natural language processing (NLP), machine learning (ML), and operationalizing AI, this role is for you!
**Responsibilities**:
- **Model Architecture and Understanding**:
- Familiarity with RNNs (Recurrent Neural Networks) and LSTMs (Long Short-Term Memory networks).
- **Responsible AI and Performance Metrics**:
- Experience working with Responsible AI practices, including validators for toxicity,



profanity, bias, and prompt injection.
- Proficiency in performance metrics such as BLEU, ROUGE, MAUVE, and Bleurt.
- Familiarity with LLM-based scoring methods.
- **Model Fine-Tuning and Open Source Libraries**:
- Hands-on experience with fine-tuning LLMs using techniques like LORA, QLORA, and RLHF (Reinforcement Learning from Human Feedback).
- Proficiency in using open-source libraries such as Hugging Face's datasets, ragas, textstat, torch, and Huggingface.
- **RAG Use Cases and Anthropic Collaboration**:
- Collaborate with the Anthropic team and work on RAG (Retrieval-Augmented Generation) use cases.
- **AWS and Platform Experience**:
- Strong working knowledge of AWS (Amazon Web Services) for deploying and scaling ML models.
- Hands-on experience with cloud-based infrastructure and services.
- **MLOPS and Engineering Pipelines**:
- Design, implement, and maintain ML engineering pipelines.
- Ensure smooth model deployment, monitoring, and maintenance.
**Qualifications**:
- **Experience**:
- Minimum 3 years of hands-on experience in LLMOPS/MLOPS roles.
- Demonstrated expertise in TensorFlow, PyTorch, Langchain, Scikit-learn (Sklearn), Argo, Docker, Spark, Postgres, and Redis.
- Full lifecycle experience with classical ML models and LLMs, including EDA (Exploratory Data Analysis), training, inference, monitoring, and fine-tuning.
- Code-heavy experience with designing and implementing ML pipelines.
**Job Type**: Contract
Pay: $****** - $****** per hour

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