Job Description

GE HealthCare’s Chief Data and Analytics Office (CDAO) delivers innovative data, insights, and AI solutions across the organization. Our Enterprise AI team drives a diverse portfolio of Machine Learning (ML), Artificial Intelligence (AI), and Generative AI (GenAI) initiatives by combining agile execution with industry-leading methods and tools.

We're seeking a highly skilled and motivated engineer experienced in ML and GenAI operations, software development, and AI architecture to join our dynamic and growing team.

As an AIOps and DevOps, you will be at the forefront of operationalizing advanced Machine Learning and Generative AI solutions. You will design, deliver, and maintain robust development and deployment pipelines for high-impact AI applications across key business domains within GE HealthCare — including Finance, Commercial, Supply Chain, Quality, Operational Excellence, Lean, and Manufacturing.

Core Responsibilities

• Develop and operationalize ML and GenAI pipelines to enable scalable, reliable, and secure deployment of AI models across GE HealthCare’s enterprise landscape,

• Automate model lifecycle management, including model versioning, continuous integration (CI/CD), testing, deployment, observability and monitoring, and governance in alignment with enterprise standards,

• Partner with IT and cloud teams to optimize infrastructure for AI workloads across hybrid and multi-cloud environments (AWS, Azure),

• Collaborate with cross-functional teams — including data scientists, software engineers, architects, and domain experts — to ensure smooth end-to-end delivery of AI solutions,

• Integrate Generative AI capabilities (e.g., LLMs, multimodal models) into business workflows, enhancing automation, productivity, and decision intelligence,

• Conduct research and proof-of-concepts to evaluate emerging tools, frameworks, and architectures for GenAI and ML Ops (e.g., LangChain, MLflow, Kubeflow, MS Copilot, OpenAi Agent Builder),

• Mentor and guide data science and engineering teams on best practices in productionizing AI models and managing their lifecycle,

• Promote a culture of innovation, collaboration, and continuous improvement within the Enterprise AI team.

Experience & Qualifications Requirements

• PhD or Master’s degree in Computer Science, Data Science, Engineering, or a related discipline with a strong focus on engineering and ML/Dev Ops,

• Advanced hands-on experience in developing, deploying, and maintaining ML/AI development pipelines and applications in enterprise environments,

• Proficiency in Python, cloud platforms (AWS, Azure), containerization, CI/CD, and DevOps practices (Docker, Kubernetes, GitHub Actions, Jenkins),

• Working knowledge of MLOps GenAIOps tools and frameworks (e.g., MLflow, SageMaker, Bedrock , LangSmith, LangGraph),

• Proven ability to translate research and prototypes into scalable enterprise-grade solutions,

• Excellent communication, collaboration, and stakeholder management skills, with the ability to influence both technical and executive audiences,

• Curiosity and drive for continuous learning, staying current with advances in GenAI, MLOps, and AI infrastructure technologies,

• Experience with vector databases (e.g., Pinecone, FAISS, Milvus) and retrieval-augmented generation (RAG) pipelines,

• Experience with LLM prompt engineering and LangChain architecture,

• Strong understanding of multi-agent or distributed AI ecosystems, enabling consistent model-to-model communication (MCP, A2A) and orchestration.

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Pay Range

For Poland based positions, Annual Salary Range:356,000.00PLN-489,500.00PLN

Placement within this range depends on:

• Relevant skills and qualifications

• Prior job-related experience

• Internal equity considerations (alignment with colleagues in similar roles) e.t.c.

We review pay ranges regularly to ensure they remain competitive with the external market and align with our internal equity considerations.

Benefits & Rewards

In addition to base salary, our employees have access to a comprehensive package of benefits and allowances, which may include:

• Health & wellness coverage

• Retirement and or savings plans

• Allowances or benefits to support role requirements (e.g., mobility, transport, or role-specific needs such as a company car or allowance where applicable)

• Work-life balance support (e.g., flexible working, leave programs)

• Recognition and incentive programs aligned with performance and company success

The exact benefits package depends on the role, location, and employment terms as specified in the Colleague Value Proposition document that will be shared prior to the interview or at the offer discussion stage.

• Performance Bonus: Details to be shared during offer discussions

Additional Information

Relocation Assistance Provided: No