Job Description
Machine Learning Engineer – LLM Focus
Location: Dallas
Salary: $115,000 – $130,000 base + 15% Bonus
Company Overview:
Join a fast-growing, innovative organization at the forefront of artificial intelligence, committed to pushing boundaries in generative AI and Large Language Models (LLMs). They are solving cutting-edge problems with scalable machine learning and deploying solutions across industries.
Role Overview:
As a Senior Machine Learning Engineer with a specialization in LLMs, you will play a pivotal role in architecting, building, and deploying high-impact machine learning systems. You’ll collaborate cross-functionally with data scientists, research engineers, and business stakeholders to drive real-world applications of advanced AI.
Key Responsibilities:
- Lead development of ML/LLM solutions for tasks like summarization, classification, Q&A, and RAG.
- Collaborate on transformer models (e.g., GPT, LLaMA, Claude, Mistral).
- Fine-tune and optimize pre-trained LLMs using best practices.
- Build and maintain ML pipelines with MLFlow, Airflow, or Kubeflow.
- Partner with MLOps/DevOps to ensure scalable, secure production systems.
- Deploy models using Docker, Kubernetes, and serving frameworks (e.g., TensorFlow Serving, TorchServe, FastAPI).
- Implement model versioning, blue-green/canary deployments, and performance monitoring.
- Develop scalable data pipelines for text and embeddings.
- Stay up to date with LLM/AI research and apply findings to real-world problems.
- Document workflows and support knowledge sharing across the team.
Required Qualifications:
- MSc or PhD in Computer Science, Machine Learning, Engineering, Mathematics, or related STEM field.
- Proven experience with LLMs and transformer-based architectures (e.g., BERT, RoBERTa, GPT, T5).
- Expertise in developing and deploying ML models in production environments.
- Strong Python programming skills; familiarity with ML/AI libraries (Hugging Face Transformers, TensorFlow, PyTorch).
- Experience with cloud platforms (AWS preferred), container orchestration (Kubernetes), and distributed data processing (Apache Spark, Kafka).
- Hands-on experience with ML tools including MLFlow, Airflow, and experiment tracking systems.
- Solid understanding of DevOps and CI/CD pipelines for ML systems.
- Strong communication skills with the ability to articulate technical details to non-technical stakeholders.
Preferred Experience:
- Experience in retrieval-augmented generation (RAG), vector databases (e.g., Pinecone, FAISS, Weaviate), and embedding models.
- Exposure to open-source LLM deployment frameworks like LangChain or LlamaIndex.
- Knowledge of reinforcement learning from human feedback (RLHF), prompt engineering, and evaluation metrics for generative models.
- Prior work in regulated or high-security environments (finance, healthcare, etc.) is a plus.
Compensation and Benefits:
- Base Salary: $115,000 – $130,000
- 15% Annual Bonus
- hybrid work setup
- Generous benefits package including health, dental, and vision insurance
- Professional development budget and opportunities to attend top AI conferences
How to Apply:
To express your interest in this opportunity, please submit your CV via the "Apply" link on this page. We look forward to hearing from you!
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