Artificial Intelligence Engineer

Datacurate Technologies; Mumbai; Hybrid; INR 1,800,000 - 2,800,000; Full time

Job summary

Datacurate Technologies is seeking an Artificial Intelligence Engineer to lead end-to-end AI/ML use cases, from problem framing to production deployment. You will build models for fraud detection, churn, credit risk, and propensity scoring while delivering GenAI-powered applications and intelligent document processing.

You will collaborate with product, operations, and data engineering teams, manage model lifecycle, and ensure production readiness with robust monitoring and evaluation.

Qualifications

• Must have hands-on experience building and deploying ML models and GenAI applications.

Responsibilities

• Design, build, and deploy ML models for business-critical use cases such as fraud detection, churn, and risk scoring.

Skills

ML & GenAI expertise

Python programming

Data science fundamentals

NLP techniques

Communication with stakeholders

Hybrid work capability

Education

Bachelor's or Master's in CS/AI/DS

Tools

OpenAI API

LangChain

Hugging Face

MLflow

W&B

CI/CD for ML

Job 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.