—
Sr AI Engineer Location: Hybrid - New York, NY Compensation: $120,000–$172,000 + bonus A large Fortune 500 insurance company is expanding its AI & Data function. This organization has spent decades building a reputation for trust, long-term thinking, and a people-first culture. Today, they’re investing heavily in modern AI capabilities to support a diversified portfolio across insurance, financial products, and emerging digital initiatives. You’ll join a well-supported AI group that combines product managers, data scientists, AI engineers, platform engineers, and model governance specialists. The team works on a wide range of AI and GenAI initiatives, partnering closely with business stakeholders from ideation through deployment. Leadership buy-in is strong, and the environment encourages experimentation, collaboration, and continuous learning. What You’ll Do • Design and build production-grade AI and ML solutions. • Ensure AI code meets engineering best practices and high-quality standards. • Implement, optimize, debug, scale, and monitor ML models in production environments. • Work with Platform Engineers to automate deployment pipelines and tooling. • Collaborate on solution architecture, ensuring ML systems integrate seamlessly into real-world applications. What You’ll Bring • 5+ years of software engineering experience, including 2+ years building AI/ML solutions. • Strong Python skills and background with ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn, etc.). • Experience developing APIs and applying modern engineering best practices. • Knowledge of NLP tooling (SpaCy, NLTK, Hugging Face). • Familiarity with AWS cloud environments and cloud-native tools. Preferred Experience • Previous exposure to Generative AI projects. • Experience with vector databases. • Kubernetes knowledge. • Experience working with MCP servers.
| Skill | Source | Confidence |
|---|---|---|
| Python | llm_hard |
100%
|
| Kubernetes | llm_hard |
100%
|
| TensorFlow | llm_hard |
100%
|
| PyTorch | llm_hard |
100%
|
| Scikit-learn | llm_hard |
100%
|
| Keras | llm_hard |
100%
|
| NLP | llm_hard |
100%
|
| Model Deployment | llm_hard |
100%
|
| MLOps | llm_hard |
100%
|
| AWS (SageMaker, EC2, S3) | llm_hard |
100%
|
| Vector Databases | llm_hard |
100%
|
| Skill | Source | Confidence |
|---|---|---|
| Collaboration | llm_soft |
100%
|
| Continuous Learning | llm_soft |
100%
|
| Query | Country | Status | Response ms | Created |
|---|---|---|---|---|
| Sr AI Engineer | extracted | 4232 | 2026-03-28 10:58 | |
| Sr AI Engineer | classified | 488 | 2026-03-28 10:24 | |
| machine learning engineer | gb | processed | 16939 | 2026-03-28 10:08 |
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"job_description": "Sr AI Engineer\n\nLocation: Hybrid - New York, NY\n\nCompensation: $120,000–$172,000 + bonus\n\nA large Fortune 500 insurance company is expanding its AI & Data function. This organization has spent decades building a reputation for trust, long-term thinking, and a people-first culture. Today, they’re investing heavily in modern AI capabilities to support a diversified portfolio across insurance, financial products, and emerging digital initiatives.\n\nYou’ll join a well-supported AI group that combines product managers, data scientists, AI engineers, platform engineers, and model governance specialists. The team works on a wide range of AI and GenAI initiatives, partnering closely with business stakeholders from ideation through deployment. Leadership buy-in is strong, and the environment encourages experimentation, collaboration, and continuous learning.\n\nWhat You’ll Do\n• Design and build production-grade AI and ML solutions.\n• Ensure AI code meets engineering best practices and high-quality standards.\n• Implement, optimize, debug, scale, and monitor ML models in production environments.\n• Work with Platform Engineers to automate deployment pipelines and tooling.\n• Collaborate on solution architecture, ensuring ML systems integrate seamlessly into real-world applications.\n\nWhat You’ll Bring\n• 5+ years of software engineering experience, including 2+ years building AI/ML solutions.\n• Strong Python skills and background with ML frameworks (TensorFlow, PyTorch, Keras, scikit-learn, etc.).\n• Experience developing APIs and applying modern engineering best practices.\n• Knowledge of NLP tooling (SpaCy, NLTK, Hugging Face).\n• Familiarity with AWS cloud environments and cloud-native tools.\n\nPreferred Experience\n• Previous exposure to Generative AI projects.\n• Experience with vector databases.\n• Kubernetes knowledge.\n• Experience working with MCP servers.",
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