Key Responsibilities
As a Data Architect with Generative AI expertise, you will :
- Design and implement robust data architectures that support AI and machine learning (ML) workloads, including Generative AI applications.
- Develop and optimize data pipelines for training, validating, and deploying AI models efficiently and securely.
- Integrate AI frameworks into data platforms, ensuring scalability, low latency, and high throughput.
- Collaborate with data scientists, AI engineers, and stakeholders to align data strategies with business goals.
- Lead initiatives to ensure data governance , security, and compliance standards (e.g., GDPR, CCPA) are met in AI-driven environments.
- Prototype and implement architectures that utilize generative models (e.g., GPT, Stable Diffusion) to enhance business processes.
- Stay up to date with the latest trends in Generative AI, data engineering, and cloud technologies to recommend and integrate innovations.
Required Qualifications
We’re looking for someone with :
A bachelor’s degree in Computer Science, Data Engineering, or a related field (master’s preferred).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 .Strong domain expertise in Life Science IndustryExperience 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.