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 :

Were looking for someone with :

- A bachelors degree in Computer Science, Data Engineering, or a related field (masters preferred).

- 10+ 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.