Required Qualifications
We’re looking for someone with:
5+ years of experience in data architecture, with a focus on AI/ML-enabled systems.
Hands-on experience with Generative AI models (e.g., OpenAI GPT, BERT, or similar), including fine-tuning and deployment.
Proficiency in data engineering tools and frameworks, such as Apache Spark, Hadoop, and Kafka.
Deep knowledge of database systems (SQL, NoSQL) and cloud platforms (AWS, Azure, GCP), including their AI/ML services (e.g., AWS Sagemaker, Azure ML, GCP Vertex AI).
Strong understanding of data governance, MLOps, and AI model lifecycle management.
Experience with programming languages such as Python or R and frameworks like TensorFlow or PyTorch.
Excellent problem-solving and communication skills, with a demonstrated ability to lead cross-functional teams.
Preferred Skills
Familiarity with LLM fine-tuning, prompt engineering, and embedding models.
Robust domain expertise in Life Science Industry
Experience integrating generative AI solutions into production-level applications.
Knowledge of vector databases (e.g., Pinecone, Weaviate) for storing and retrieving embeddings.
Expertise in APIs for AI models, such as OpenAI API or Hugging Face Transformers.