Work Schedule

Environmental Conditions

Job Description

About Team:

Digital Engineering is Thermo Fisher’s “Software Engineering” center of excellence. We build cloud computing services, products and Platforms that the scientific community demands. Using the emerging technologies with the potential to significantly change lab workflows and facilitate access to powerful data analysis techniques. Our division serves as an extension of Thermo Fisher’s Software R&D teams while enhancing our responsiveness and attention in enabling our customers to make the world healthier, cleaner and safer. Thermo Fisher was built to serve society and that sense of purpose will continue to enhance innovation and scientific advancement. We are committed to diversity in our workforce and is proud to be an equal opportunity employer

We apply industry standard methodologies to the design, development, and deployment of world- class software products built to demonstrate the power and scalability of the cloud.

Roles & Responsibilities:

Purpose:

The Software Staff Engineer provides software and systems architectural and design leadership to multiple Software Development Scrum Teams delivering a Gen AI Solution.

As a Lead Developer, this individual will actively contribute to the architecture, design, and development of new features, integrating Generative AI systems to enhance functionality. The role involves working with AI frameworks and models, ensuring seamless integration with existing product offerings. In addition to that, the role also demands to lead, empower, mentor, and provide ample guidance to the other team members with respect to technical challenges.

Responsibilities:

• Provide software and systems architectural and design leadership to team of engineers. Design and implement cloud service and software architecture for new products, and extensions to existing products. Be the team’s “go-to” person for architectural, design and implementation related questions and provide guidance.
• Active contribution in solution analysis of requirements
• Able to understand, articulate and challenge the requirements
• Provides guidance regarding design activity to other programmers on technical aspects relating to the project.
• Proactively looks for ways and patterns to continuously automate feature testing with measurable and tangible goals
• Is authentic, transparent and leads by example, holding self and others accountable. Inspires, motivates and collaborates with others
• Anticipates needs and problems while creating solutions. Is willing to ask difficult questions and do things differently. Greets challenge and change as opportunity
• A highly motivated fast learner who can self-start and "determine what needs figuring out”
• Actively participates in development community of practices in sharing and learning standard processes, leads initiatives within the community and involve in other organization initiatives
• Excellent verbal and written communication skills. Ability to effectively document artifacts and processes then explain them to others

Candidate Requirement:

Education : Bachelors in Engineering or Masters in Computer Science with 10+ years of extensive experience.

Mandatory Skills, Knowledge, and Experience:

• Python Development Experience:
  Minimum 6 years of proven experience in Python development, with a strong emphasis on backend development, including creating RESTful APIs, working with libraries like FastAPI for high-performance web services.
• Generative AI (Gen AI) & OpenAI Integration:
  Hands-on experience with Generative AI frameworks and APIs, including OpenAI models for generating human-like responses, completing tasks, and automating processes. Knowledge of how to effectively integrate these models into applications.
• API Development & Integration:
  Extensive experience in building and maintaining REST APIs using FastAPI, ensuring efficient communication between different services and applications. Familiarity with authentication, authorization, and API rate-limiting.
• Data Engineering & Processing:
  Strong skills in data engineering, including data extraction, transformation, and loading (ETL) processes. Expertise in Pandas for data manipulation, analysis, and handling large datasets.
• LLM (Large Language Model) Prompt Engineering:
  Experience in prompt engineering for LLMs. Ability to design and optimize prompts for specific use cases to extract relevant, high-quality outputs.
• Python Data Science Libraries:
  Strong proficiency in Pandas, NumPy, and other data analysis libraries to process and manipulate large volumes of data. Experience in generating data insights and performing statistical analyses.
• Version Control & CI/CD:
  Proficient in using Git for version control and familiar with CI/CD pipelines for automated testing and deployment processes.
• Scrum and Agile Methodologies:
  3+ years of experience with Scrum or Agile-based software development methodologies, with a focus on iterative development and collaboration.
• Testing and Automation:
  Experience in unit testing, integration testing, and automated testing using frameworks like pytest and unittestto ensure code quality and reliability.
• Communication & Documentation:
  Excellent verbal and written communication skills, capable of detailing code, technical processes, and explaining them to both technical and non-technical collaborators.
• Non-Functional Requirements (NFR):
  Experience in defining and implementing Non-Functional Requirements such as performance optimizations, scalability, and security in data-driven applications.

Nice to Have Skills, Knowledge, and Experience:

• Cloud Services & Deployment:
  Experience with cloud platforms like AWS or GCP, specifically related to data storage, serverless computing, and scalable APIs.
• Data Pipeline Tools:
  Familiarity with data pipeline tools like Apache Airflow, Apache Kafka, or similar platforms for managing and orchestrating data workflows.
• Machine Learning & AI Frameworks:
  Experience with machine learning libraries such as scikit-learn, TensorFlow, PyTorch, or similar, particularly in building and training models for data-driven applications.
• Code Quality & Analysis Tools:
  Experience with SonarQube, ESLint, or similar tools for code quality analysis, ensuring maintainability and scalability of the codebase.