About the Role:

We are looking for a Senior Product AI Data Engineer to build and scale AI-powered data capabilities for products serving the Life Sciences, Healthcare, Biotech, and Pharmaceutical ecosystem.

You will work at the intersection of data engineering, AI engineering, and product development, developing reliable data pipelines, ML workflows, Generative AI capabilities, and intelligent data experiences.

Key Responsibilities:

- Develop production-grade data and AI solutions that support healthcare and medical device intelligence products.

- Build scalable batch and distributed data processing pipelines using Python, PySpark, Databricks, and Delta Lake.

- Develop robust ETL/ELT workflows and orchestration pipelines using Apache Airflow.

- Design data models and optimize SQL workloads across Snowflake, PostgreSQL, Oracle, and Databricks.

- Develop data pipelines that prepare high-quality datasets for machine learning and AI applications.

- Collaborate with Data Scientists to productionize machine learning models and implement feature engineering pipelines.

- Build model-serving and inference pipelines supporting AI-enabled product features.

- Develop Generative AI capabilities using LLMs, RAG, embeddings, vector databases, and AI workflows.

- Contribute to the development of Agentic AI solutions that automate complex data and business workflows.

- Build ingestion and retrieval pipelines for enterprise knowledge and domain-specific datasets.

- Implement data quality, validation, monitoring, lineage, and governance mechanisms.

- Optimize data pipelines for performance, reliability, scalability, and cost.

- Support MLOps activities including model deployment, monitoring, retraining, and operationalization.

- Work with cloud platforms such as AWS or Azure to implement scalable data and AI workloads.

- Participate in technical design discussions and contribute to architectural decisions.

- Review code, improve engineering practices, and provide technical guidance to other engineers.

- Work closely with Product Managers, Business Analysts, Architects, and Analytics teams to understand requirements and deliver measurable outcomes.

Technical Skills:

- Strong programming experience in Python.

- Hands-on experience with PySpark, Databricks, Snowflake, Delta Lake, and Apache Airflow.

- Strong SQL and experience working with relational and analytical databases.

- Experience with AWS or Azure and cloud-based data engineering solutions.

- Strong understanding of data pipelines, distributed computing, data modeling, ETL/ELT, and data quality.

- Practical experience with machine learning pipelines and MLOps.

- Experience with Generative AI, LLM integration, RAG, embeddings, vector databases, or AI agents.

- Knowledge of API development and integration of AI capabilities into enterprise applications is an advantage.

- Experience with SpecKit, OpenSpec, or similar specification-driven development approaches is desirable.

Domain & Collaboration:

- Exposure to Healthcare, Life Sciences, Pharmaceutical, Biotechnology, or Medical Device data is preferred.

- Understanding of data governance, privacy, security, and regulatory requirements is an advantage.

- Ability to communicate technical concepts effectively with engineering, product, analytics, and business stakeholders.

Qualifications:

- Bachelor's or Master's degree in Computer Science, Software Engineering, Data Engineering, AI, or a related field.

- 8 - 10 years of experience in software engineering, data engineering, AI engineering, or related areas.

- Experience delivering production-grade data and AI solutions in enterprise environments.

Success in this Role:

- Success will be demonstrated through reliable AI/data pipelines, high-quality data products, efficient AI workloads, successful production deployments, improved product capabilities, and measurable value delivered to customers.