Principal Data/AI Engineer

Jobgether

2d•Remote

About The Position

This role offers the chance to lead the design, development, and operational excellence of enterprise-scale data and AI platforms that power mission-critical analytics and AI-driven solutions. You will drive technical strategy and architecture for data pipelines, machine learning, and generative AI initiatives, while ensuring scalability, performance, and regulatory compliance. The position requires hands-on expertise in building batch and streaming pipelines, integrating biotech systems, and delivering AI/ML solutions on cloud platforms. You will mentor engineers, establish best practices, and work cross-functionally with product managers, platform architects, and stakeholders to enable modern AI use cases. This is a high-impact role where technical excellence, innovation, and collaboration are essential to delivering reliable and scalable data solutions.

Requirements

Bachelorâs degree in Computer Science, Data Engineering, AI/ML Engineering, or a related field (advanced degree preferred)
12+ years of experience in data engineering, AI/ML engineering, or cloud platform engineering
Proven expertise in Python, SQL, dbt, and modern orchestration tools (Airflow or equivalent)
Experience designing data models for analytics and AI/ML applications
Hands-on experience delivering production-grade data solutions in cloud environments (AWS, Azure)
Familiarity with biotech or life sciences systems and regulatory compliance frameworks (GxP, FDA, EMA)
Strong problem-solving, optimization, and troubleshooting skills for large-scale, complex data systems
Effective communication skills for collaborating with technical and non-technical stakeholders, including senior leadership

Nice To Haves

MS or PhD in relevant technical fields
Industry certifications (e.g., Snowflake, AWS, Azure)
Experience with LLMs, embeddings, vector databases, containerized workloads (Docker/Kubernetes)
Demonstrated passion for emerging technologies, continuous improvement, and building innovative engineering cultures

Responsibilities

Architect, build, and maintain scalable batch and streaming data pipelines using Snowflake, Airflow, dbt, and other modern frameworks
Deliver ML and Generative AI solutions, including feature stores, vector search, model serving, and monitoring for drift, bias, and performance
Integrate biotech and life sciences systems (MES, LIMS, SCADA, ERP, QMS) into centralized data platforms
Define and enforce data quality, observability, lineage, and governance standards across pipelines and platforms
Optimize cloud infrastructure (AWS, Azure), storage tiers, compute clusters, and orchestration for latency, throughput, and cost efficiency
Lead design reviews, incident postmortems, cross-team architecture discussions, and mentoring of junior engineers
Stay current with emerging technologies such as data mesh, real-time streaming, digital twins, and generative AI, and introduce innovations to the platform