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Work Schedule Standard (Mon-Fri) Environmental Conditions Office 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. REQUIREMENTS: Education: Preferred Fields of Study: Computer Science, Information Technology, Data Science, Artificial Intelligence, Engineering or related technical field 3+ 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 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.
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.