Data Architect- Generative AI
2 weeks ago
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 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.
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