AI Engineer
Role details
Job location
Tech stack
Job description
- Design, develop, test, and maintain full-stack Python-based AI applications.
- Build and deploy GenAI solutions utilizing Retrieval-Augmented Generation (RAG) frameworks in production environments.
- Develop and optimize document ingestion pipelines and data processing workflows.
- Implement and manage vector database solutions, including MongoDB Atlas.
- Collaborate with product, engineering, and business teams to deliver scalable and reliable AI solutions.
- Participate throughout the software development lifecycle, from architecture and design through deployment and maintenance.
- Deploy, monitor, and maintain AI applications on cloud platforms such as Azure and Google Cloud Platform.
- Integrate Agentic AI and Model Context Protocol (MCP) technologies into enterprise AI solutions.
- Support AI use cases involving large-scale data processing, reconciliation, and intelligent automation.
- Create technical documentation and communicate effectively with both technical and non-technical stakeholders.
Requirements
The ideal candidate will possess strong expertise in AI technologies, full-stack Python development, GenAI application architecture, document processing pipelines, and cloud-native AI deployments. This role requires hands-on development experience and the ability to deliver scalable, production-grade AI solutions while collaborating with cross-functional teams and business stakeholders., * 8+ years of hands-on software engineering and programming experience with a focus on AI and Machine Learning.
- Strong expertise in Generative AI (GenAI), Agentic AI, and Large Language Models (LLMs).
- Experience working with public cloud platforms, preferably Azure, including Azure AI services and tools.
- Strong hands-on experience in full-stack Python development, including frontend, backend, and API development.
- Proven experience designing, building, and deploying production-grade GenAI applications using Retrieval-Augmented Generation (RAG) architectures.
- Experience developing document ingestion pipelines and processing large-scale unstructured data.
- Hands-on experience with vector databases, preferably MongoDB Atlas.
- Strong understanding of software engineering best practices, scalability, and application deployment.
Preferred Qualifications
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Experience working within healthcare enterprises or healthcare-related AI projects.
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Experience designing and developing AI/ML models for:
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Predictive analytics
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Forecasting
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Anomaly detection
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Data-driven decision-making
Experience tuning and optimizing machine learning models.
Experience working with SAP environments and integrations.
Exposure to Google Cloud Platform (Google Cloud Platform).