We are seeking a skilled and passionate AI/ML Engineer with a strong focus on large language model (LLM) fine-tuning and adaptation. The ideal candidate will bring expertise across the end-to-end ML lifecycle—from data preprocessing and model development to deployment, optimization, and monitoring.

You will play a critical role in shaping and refining advanced AI systems that our clients and medical experts will rely on for accurate, efficient, and trustworthy decision support.

This role requires not only a solid foundation in machine learning and data engineering, but also hands-on experience with LLM fine-tuning, parameter-efficient training methods (LoRA/QLoRA), and model evaluation techniques.

You will collaborate closely with cross-functional teams to deliver domain-specific AI solutions that balance innovation, performance, and reliability in high-stakes environments.

Key Responsibilities

Data Preparation & Domain Adaptation: Collect, preprocess, and structure complex, unstructured medical and clinical data (e.g., records, reports, clinical notes) for training and fine-tuning, while ensuring compliance with privacy and security standards (HIPAA, GDPR).
Model Development & LLM Fine-Tuning: Design, adapt, and fine-tune Large Language Models (LLMs) for specialized tasks such as summarization, information extraction, semantic search, and classification. Apply parameter-efficient approaches (LoRA/QLoRA, PEFT) to customize models for domain-specific applications.
Model Exploration & Benchmarking: Stay ahead of the curve by evaluating cutting-edge open-source LLMs (e.g., Llama, Mistral, Gemma). Experiment with novel architectures, fine-tuning methods, and inference optimizations to identify the most effective solutions for real-world use cases.
MLOps & Scalable Deployment: Build and maintain robust MLOps pipelines for training, deployment, and scaling of models. Use containerization, orchestration (Docker/Kubernetes), and CI/CD workflows to ensure reproducibility, reliability, and fast iteration.
Performance Monitoring & Continuous Improvement: Establish monitoring frameworks to track model accuracy, drift, and robustness. Work with domain experts to validate outputs and drive iterative improvements based on feedback.
Responsible & Ethical AI: Ensure fairness, transparency, and explainability in all deployed AI systems. Conduct bias audits and document decision processes to uphold ethical best practices.
Cross-Functional Collaboration: Partner with engineers, product managers, and domain experts to translate requirements into impactful AI solutions. Communicate complex AI concepts clearly to both technical and non-technical stakeholders.

Preferred Skills & Experience

Bachelor’s or master’s degree in computer science, Data Science, or a related quantitative field.
Proven experience as a Machine Learning Engineer, Data Scientist, or similar role, with hands-on experience in building and deploying production-grade ML systems.
Strong programming skills in Python, with proficiency in frameworks such as PyTorch or TensorFlow.
Experience in LLM fine-tuning and generative AI models, including parameter-efficient approaches (LoRA, QLoR), instruction tuning, supervised fine-tuning (SFT), and reinforcement learning methods (RLHF, DPO, GRPO).
Hands-on experience with LLM frameworks and libraries: Hugging Face Transformers, DeepSpeed, bitsandbytes, vLLM.
Familiarity with retrieval-augmented generation (RAG), LangChain/LlamaIndex, and vector databases.
Skilled in model evaluation and benchmarking, including task-specific metrics, human evaluation, and bias/fairness testing.
Knowledge of distributed training, GPU optimization, and quantization (4-bit, 8-bit) for efficient model adaptation.
Hands-on experience with MLOps practices and tools for deployment (e.g., vLLM, Ray Serve), monitoring (e.g., Evidently AI, Prometheus/Grafana), experiment tracking and model versioning (e.g., MLflow, Weights & Biases), and workflow orchestration (e.g., Apache Airflow).
Familiarity with cloud platforms. Oracle Cloud Infrastructure (OCI) experience is a strong plus.
Strong problem-solving skills and ability to collaborate with subject matter experts to translate complex domain knowledge into machine learning solutions.
Understanding of data privacy and security principles, especially when working with sensitive or regulated data (e.g., PHI).

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