Sr AI Engineer

Work Schedule

Standard (Mon-Fri)

Environmental Conditions

Office

Job Description

As part of the Thermo Fisher Scientific team, you’ll discover meaningful work that makes a positive impact on a global scale. Join our colleagues in bringing our Mission to life every single day to enable our customers to make the world healthier, cleaner and safer. We provide our global teams with the resources needed to achieve individual career goals while helping to take science a step beyond by developing solutions for some of the world’s toughest challenges, like protecting the environment, making sure our food is safe or helping find cures for cancer.

DESCRIPTION:

Join our innovative team as a AI Engineer and make a meaningful impact on global science and healthcare. In this role, you'll design and develop sophisticated software solutions that contribute to improving health and environmental outcomes. You'll work with cutting-edge technologies across cloud platforms, data integration, AI/ML, and enterprise systems to deliver robust applications that power our essential operations.

Working in an agile environment, you'll collaborate with cross-functional teams to architect, implement and optimize solutions across various platforms including AWS, Azure, ServiceNow, and enterprise systems. You'll have the opportunity to drive technical innovation while supporting the growth of other developers and establishing best practices for software development excellence.

Key Responsibilities

Design, develop, and deploy AI automation solutions using LLMs and generative AI technologies.

Build, configure, and integrate Model Context Protocol (MCP) servers and tools for connecting LLMs with enterprise systems, APIs, databases, and workflows.

Develop prompt engineering strategies for reliable, reusable, and production-grade AI workflows.

Create AI agents, automation pipelines, and orchestration flows for business process automation.

Work with business stakeholders to understand requirements and translate them into AI-enabled technical solutions.

Build integrations with enterprise systems, APIs, databases, cloud services, and internal platforms.

Design, load, and query knowledge graphs using AWS Neptune or other graph databases.

Develop backend logic, data pipelines, and automation scripts using Python.

Write and optimize SQL queries for data extraction, validation, transformation, and reporting.

Apply basic machine learning concepts where required, including classification, regression, clustering, feature engineering, and model evaluation.

Support deployment, monitoring, testing, troubleshooting, and documentation of AI automation solutions.

Ensure AI solutions are secure, scalable, explainable, maintainable, and aligned with enterprise standards.

Collaborate with data engineering, application, infrastructure, architecture, security, and business teams.

Leading and managing teams

REQUIREMENTS:

Education:

Preferred Fields of Study: Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering or related technical field

5+ years of software engineering, data engineering, AI engineering, automation engineering, or related technology experience.

Fluent in English and Desired Spanish

Experience:

Hands-on experience working with LLMs, generative AI, AI agents, or AI automation solutions.

Proven experience building production-grade Python applications, automation scripts, APIs, or data processing workflows.

Experience integrating AI solutions with databases, APIs, enterprise applications, and cloud services.

Project-based delivery experience is preferred.

Strong analytical and problem-solving abilities

Excellent communication and collaboration skills

Experience with AI/ML technologies and frameworks preferred

Knowledge of security best practices and compliance requirements

Ability to work independently and as part of a distributed team

Occasional travel may be required (up to 10%)

Mandatory Skills:

Strong and comprehensive knowledge on business processes (finance, operations, customer services, services, etc)

Strong hands-on experience with Large Language Models such as OpenAI, Claude, Gemini, Llama, Mistral, or similar models.

Strong experience in prompt engineering, including system prompts, few-shot prompting, chain-of-thought-safe design, structured outputs, prompt optimization, and prompt evaluation.

Mandatory hands-on experience creating MCP servers, MCP tools, and integrations for LLM-based applications.

Strong programming experience in Python, including scripting, API integration, data processing, and production-quality coding practices.

Strong working knowledge of SQL for querying, data profiling, joins, aggregations, stored procedures, and performance optimization.

Good hands-on understanding of AWS cloud services and cloud-native deployment patterns.

Good understanding of business processes and ability to convert business requirements into AI automation use cases.

Basic understanding of machine learning models and concepts, including supervised and unsupervised learning.

Experience building AI agents, Retrieval-Augmented Generation pipelines, workflow automation, or tool-using LLM applications.

Preferred Skills:

Experience with Retrieval-Augmented Generation and enterprise knowledge sources.

Experience with AI frameworks such as LangChain, LlamaIndex, Semantic Kernel, CrewAI, AutoGen, or similar.

Experience building REST APIs using FastAPI, Flask, Django REST Framework, or similar Python frameworks.

Experience with AWS services such as Amazon Bedrock, Lambda, S3, API Gateway, CloudWatch, IAM, Step Functions, ECS, EKS, SageMaker, Glue, Athena, and OpenSearch.

Experience with data engineering concepts, ETL/ELT pipelines, batch processing, API ingestion, and data quality validation.

Experience with Git, CI/CD, Docker, containerized deployments, and modern DevOps practices.

Experience with LLM evaluation frameworks for answer quality, hallucination checks, safety, reliability, grounding, and accuracy.

Knowledge of responsible AI, data privacy, access control, security, auditability, and governance practices.

Experience with observability and monitoring for AI systems, including logging, tracing, cost tracking, latency monitoring, and usage analytics.

Familiarity with workflow automation tools such as Airflow, Power Automate, n8n, Zapier, or similar platforms.