About the role
structured by ORICompany Description Datacurate Technologies is a leading information management and analytics consulting company specializing in helping organizations implement data-driven decision-making. By delivering innovative, purpose-built solutions based on industry-standard products, Datacurate accelerates business outcomes…
What you will do
- Design, build, and deploy supervised and unsupervised ML models for business-critical use cases such as fraud detection, churn prediction, and customer segmentation.
- Develop and implement GenAI use cases leveraging large language models (LLMs), including RAG pipelines, prompt engineering, fine-tuning, and agentic workflows.
- Translate business problems into well-scoped AI/ML solutions through close collaboration with product, operations, and data engineering teams.
- Manage the full model lifecycle — training, evaluation, monitoring, and retraining — ensuring models remain accurate and production-ready.
- Build and maintain feature engineering pipelines and curate high-quality training datasets.
What they are looking for
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
- Hands-on experience building and deploying ML models for classification or regression problems — fraud detection, churn, risk scoring, or similar use cases preferred.
- Practical experience with GenAI technologies: LLMs, prompt engineering, RAG architecture, vector databases, and/or fine-tuning workflows.
- Proficiency in Python and core ML libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch).
- Strong understanding of feature engineering, model evaluation metrics, and techniques to handle imbalanced datasets.
- Experience with NLP techniques and frameworks (spaCy, Hugging Face Transformers, etc.).
- Familiarity with MLOps tools and practices — model versioning, experiment tracking (MLflow, W&B), and CI/CD for ML pipelines.
- Strong problem-solving skills with the ability to communicate technical findings clearly to non-technical stakeholders.
- Ability to work independently and collaboratively in a hybrid environment.
Nice to have
- Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
- Exposure to agentic AI frameworks (LangGraph, AutoGen, CrewAI).
- Knowledge of data privacy regulations and responsible AI principles relevant to financial or customer data.
Full posting text
Company Description
Datacurate Technologies is a leading information management and analytics consulting company specializing in helping organizations implement data-driven decision-making. By delivering innovative, purpose-built solutions based on industry-standard products, Datacurate accelerates business outcomes while minimizing risk. Recognizing data as the backbone of digital transformation and artificial intelligence initiatives, the company focuses extensively on data recency, quality, and governance.
Datacurate leverages its expertise to support organizations in optimizing their data strategies to achieve sustainable growth and success.
Role Description
We are looking for an Artificial Intelligence Engineer to drive the development of machine learning models and Generative AI solutions across our business. You will own the end-to-end lifecycle of AI/ML use cases — from problem framing and data exploration through to model development, validation, and production deployment. Core focus areas include predictive models such as Fraud Detection, Customer Churn, Credit Risk, and Propensity Scoring , as well as GenAI-powered applications including intelligent assistants, document processing pipelines, and LLM-based automation.
Key Responsibilities
Design, build, and deploy supervised and unsupervised ML models for business-critical use cases such as fraud detection, churn prediction, and customer segmentation.
Develop and implement GenAI use cases leveraging large language models (LLMs), including RAG pipelines, prompt engineering, fine-tuning, and agentic workflows.
Translate business problems into well-scoped AI/ML solutions through close collaboration with product, operations, and data engineering teams.
Manage the full model lifecycle — training, evaluation, monitoring, and retraining — ensuring models remain accurate and production-ready.
Build and maintain feature engineering pipelines and curate high-quality training datasets.
Evaluate and integrate AI tools, APIs, and frameworks (e.g. OpenAI, LangChain, Hugging Face) to accelerate GenAI delivery.
Establish best practices for model explainability, bias detection, and responsible AI.
Qualifications
Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field.
Hands-on experience building and deploying ML models for classification or regression problems — fraud detection, churn, risk scoring, or similar use cases preferred.
Practical experience with GenAI technologies: LLMs, prompt engineering, RAG architecture, vector databases, and/or fine-tuning workflows.
Proficiency in Python and core ML libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch).
Strong understanding of feature engineering, model evaluation metrics, and techniques to handle imbalanced datasets.
Experience with NLP techniques and frameworks (spaCy, Hugging Face Transformers, etc.).
Familiarity with MLOps tools and practices — model versioning, experiment tracking (MLflow, W&B), and CI/CD for ML pipelines.
Strong problem-solving skills with the ability to communicate technical findings clearly to non-technical stakeholders.
Ability to work independently and collaboratively in a hybrid environment.
Nice to Have
Experience with cloud ML platforms (AWS SageMaker, GCP Vertex AI, or Azure ML).
Exposure to agentic AI frameworks (LangGraph, AutoGen, CrewAI).
Knowledge of data privacy regulations and responsible AI principles relevant to financial or customer data.
Seniority level: Entry level
Employment type: Full-time
Job function: Engineering and Information Technology
Industries: IT Services and IT Consulting