Senior Manager, AI Solutions Engineer

ROLE SUMMARY

Do you want to make an impact on patient health around the world Do you thrive in a fast-paced environment that brings together scientific clinical and commercial domains through engineering data science and AI Then join Pfizer Digitals Commercial Creation Center & CDI organization (C4) where you can leverage cutting-edge technology to inform critical business decisions and improve customer experiences for our colleagues patients and physicians. Our collection of engineering data science and AI professionals are at the forefront of Pfizers transformation into a digitally driven organization that leverages data science and AI to change patients lives. The Commercial Analytics AI Solutions and Engineering (CAASE) team is a critical driver and enabler of Pfizers digital transformation leading the process and engineering innovation to rapidly progress early AI and data science applications from prototypes and MVPs to full production and maintenance.

As a Senior Manager AI Solutions Engineer you will be a hands-on technical expert and tech lead within the CAASE team charged with architecting implementing enabling and testing AI solutions and reusable AI components. You will identify design iteratively develop and continuously improve reusable components for AI that accelerate use case delivery. You will implement best practices and maintain standards for AI application and API development data engineering and data pipelining data science and ML engineering and prompt engineering to enable understanding and re-use drive scalability and optimize addition you will be responsible for providing critical input into the AI ecosystem and platform strategy to promote self-service drive productization and collaboration and foster innovation as well as ensuring the solutions are tested and will work reliably.

ROLE RESPONSIBILITIES

• Architect and implement AI and ML solutions and reusable software components within the AWS cloud infrastructure; Ensure solutions meet the diverse needs of various use cases.

• As a tech lead enforce coding standards best practices and thorough testing (unit integration etc.) to ensure reliability and maintainability.

• Define and implement robust API and integration strategies to seamlessly connect reusable AI components with broader systems.

• Define and implement robust technical strategies in areas such as API integration to connect reusable AI components with broader systems industrialized AI accelerators and the delivery of scalable AI solutions.

• Demonstrate a proactive approach to identifying and resolving potential system issues.

• Train and guide junior developers on concepts such as data analytics machine learning AI and software development principles tools and best practices.

• Foster a collaborative learning environment within the team by sharing knowledge and expertise.

• Act as a subject matter expert for solution engineering on cross functional teams in bespoke organizational initiatives by providing thought leadership and execution support for software development needs.

• Direct research in areas such as data science software development data engineering and data pipelines and prompt engineering and contribute to the broader talent building framework by facilitating related trainings.

• Communicate value delivered through reusable AI components to end user functions (e.g. Chief Marketing Office) and evangelize innovative ideas of reusable & scalable development approaches/frameworks/methodologies to enable new ways of developing AI solutions.

• Provide strategic and technical input to the AI ecosystem including platform evolution vendor scan and new capability development.

• Partner with AI use case development teams to ensure successful integration of reusable components into production AI solutions.

• Partner with cross-functional team on end-to-end capability integration between enterprise platforms and internally developed reusable component accelerators (API registry ML library / workflow management enterprise connectors).

• Partner with cross-functional team to define best practices for reusable component architecture and engineering principles to identify and mitigate potential risks related to component performance security responsible AI and resource utilization.

BASIC QUALIFICATIONS

• Bachelors degree in AI data science or computer engineering related area (Data Science Computer Engineering Computer Science Information Systems Engineering or a related discipline)

• 7 years of work experience in data science or AI solution engineering with a track record of building deploying and testing complex software systems

• Demonstrated experience interfacing with internal and external teams to develop innovative AI and data science solutions

• Experience working in a cloud-based analytics ecosystem (AWS Snowflake etc.)

• Deep expertise spanning data science solution architecture hands-on development and testing

• Expert knowledge of backend technologies and API design principles

• Proficient in writing clean efficient and maintainable code (Python SQL)

• Strong understanding of the Software Development Life Cycle (SDLC) and data science development lifecycle (CRISP)

• Highly self-motivated to deliver both independently and with strong team collaboration

• Ability to creatively take on new challenges and work outside comfort zone

• Strong English communication skills (written & verbal)

PREFERRED QUALIFICATIONS

• Advanced degree in Data Science Computer Engineering Computer Science Information Systems or related discipline

• Demonstrated technical leadership in AI or software engineering with deep expertise in data science or backend solution architecture hands-on development and testing

• Experience in solution architecture and system design at scale

• Experience in software/product engineering

• Hands-on experience with API testing (e.g. model validation drift detection non-deterministic output testing)

• Experience with load and resilience testing for AI/ML endpoints

• Experience with CI/CD integration and automated testing pipelines

• Strong hands-on skills in ML engineering and data science (Python SQL industrialized ETL software)

• Experience with data science enabling technology such as Dataiku Data Science Studio AWS SageMaker or other data science platforms

• Deep understanding of DevOps practices and toolchains

• Hands-on experience with ML model lifecycle management (MLOps)

• Experience with distributed systems and databases

• Experience working in Agile teams processes and practices

Please apply by sending your CV in English.

Work Location Assignment:Hybrid