Newxel
We are seeking a
Machine Learning Engineer
to join a fast-growing InsurTech company that’s revolutionizing the life insurance industry through AI and predictive modeling. In this role, you’ll take full ownership of the end-to-end AI feature development cycle — from research and design to deployment in production. You’ll lead key technical initiatives, build scalable ML pipelines, and collaborate closely with data scientists and product teams to transform complex data into measurable business impact. This position offers a high level of autonomy, technical influence, and direct contribution to shaping next-generation AI products. This role is not for the passive – we need someone who owns problems, drives solutions, and isn’t afraid to challenge assumptions. If you’re passionate about leveraging data to transform an industry, we want to hear from you.
Responsibilities
Lead the design, development, and deployment of end-to-end Python-based ML pipelines
Build and optimize models for prediction, segmentation, explainability, and personalization
Develop agentic pipelines and RAG systems using frameworks like LangChain or similar
Apply MLflow, Optuna, XGBoost, and SHAP for experimentation, tuning, and explainability
Collaborate closely with data scientists, software engineers, and product stakeholders
Drive architectural and feature-level decisions with measurable product impact
Requirements
3+ years of hands-on experience in developing and deploying ML pipelines in Python
Proven ability to lead ML feature development from concept to production
Strong understanding of ML methods: feature selection, boosting, ensembling, explainable AI (XAI)
Experience with scikit-learn, pandas, numpy, xgboost, Optuna (or similar), MLflow, SHAP
1+ year of hands-on experience with LLM / GenAI systems: LangChain or similar frameworks, RAG pipelines
Theoretical understanding of transformers and LLM architectures
Experience with AWS cloud, Python IDEs (PyCharm, VSCode)
Comfortable using LLM-based dev tools (Claude Code, Cursor, etc.)
You'll fit great if you
Have experience with LLM evaluation or fine-tuning
Have experience in Insurance or Fintech – understanding industry nuances is a major advantage
Love exploring new ML and GenAI technologies and applying them in production
Think like a builder — from prototype to scalable solution
Enjoy working in a fast-paced environment with high autonomy
What we offer
Competitive salary and benefits package
Medical insurance
Full Remote
Collaborative and innovative work environment
Career growth and development opportunities
A chance to work with a talented and driven team of professionals
About the project Our client is a fast-growing InsurTech company reshaping the life insurance industry through advanced data analytics and predictive modeling. Their platform empowers insurers to make smarter, data-driven decisions — improving risk modeling, customer insights, and business performance at scale. Their R&D team brings together deep expertise in data science, machine learning, and actuarial analysis to deliver practical, actionable insights that help global insurers move from intuition to precision. With a mission to make insurance more transparent, personalized, and insight-driven, they’re redefining how the industry understands and serves its customers.
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Machine Learning Engineer
to join a fast-growing InsurTech company that’s revolutionizing the life insurance industry through AI and predictive modeling. In this role, you’ll take full ownership of the end-to-end AI feature development cycle — from research and design to deployment in production. You’ll lead key technical initiatives, build scalable ML pipelines, and collaborate closely with data scientists and product teams to transform complex data into measurable business impact. This position offers a high level of autonomy, technical influence, and direct contribution to shaping next-generation AI products. This role is not for the passive – we need someone who owns problems, drives solutions, and isn’t afraid to challenge assumptions. If you’re passionate about leveraging data to transform an industry, we want to hear from you.
Responsibilities
Lead the design, development, and deployment of end-to-end Python-based ML pipelines
Build and optimize models for prediction, segmentation, explainability, and personalization
Develop agentic pipelines and RAG systems using frameworks like LangChain or similar
Apply MLflow, Optuna, XGBoost, and SHAP for experimentation, tuning, and explainability
Collaborate closely with data scientists, software engineers, and product stakeholders
Drive architectural and feature-level decisions with measurable product impact
Requirements
3+ years of hands-on experience in developing and deploying ML pipelines in Python
Proven ability to lead ML feature development from concept to production
Strong understanding of ML methods: feature selection, boosting, ensembling, explainable AI (XAI)
Experience with scikit-learn, pandas, numpy, xgboost, Optuna (or similar), MLflow, SHAP
1+ year of hands-on experience with LLM / GenAI systems: LangChain or similar frameworks, RAG pipelines
Theoretical understanding of transformers and LLM architectures
Experience with AWS cloud, Python IDEs (PyCharm, VSCode)
Comfortable using LLM-based dev tools (Claude Code, Cursor, etc.)
You'll fit great if you
Have experience with LLM evaluation or fine-tuning
Have experience in Insurance or Fintech – understanding industry nuances is a major advantage
Love exploring new ML and GenAI technologies and applying them in production
Think like a builder — from prototype to scalable solution
Enjoy working in a fast-paced environment with high autonomy
What we offer
Competitive salary and benefits package
Medical insurance
Full Remote
Collaborative and innovative work environment
Career growth and development opportunities
A chance to work with a talented and driven team of professionals
About the project Our client is a fast-growing InsurTech company reshaping the life insurance industry through advanced data analytics and predictive modeling. Their platform empowers insurers to make smarter, data-driven decisions — improving risk modeling, customer insights, and business performance at scale. Their R&D team brings together deep expertise in data science, machine learning, and actuarial analysis to deliver practical, actionable insights that help global insurers move from intuition to precision. With a mission to make insurance more transparent, personalized, and insight-driven, they’re redefining how the industry understands and serves its customers.
#J-18808-Ljbffr