3+ years of experience as an AI/ML Engineer
Expertise in Python programming and experience with ML packages such as scikit-learn, pandas, and numpy for building and training models
Proficiency in writing SQL queries to extract and manipulate data for model training and analysis
Experience with predictive analytics and fraud detection models, including Logistic Regression, Gradient Boosting (eg, XGBoost), and Random Forest
Familiarity with agentic AI frameworks such as LangChain and LangGraph
Strong communication skills to articulate complex technical concepts to both technical and non-technical stakeholders
Familiarity with cloud platforms (AWS, GCP, or Azure) is a plus
Experience with version control systems (Git) and big data tools (eg, Spark) is an advantage
What You Will Do Design, develop, and deploy robust machine learning models in Python to solve real-world business challenges
Collaborate with a team of engineers and engineering leaders to implement the technical AI/ML strategy for clients across industries such as fintech, logistics, and hospitality
Drive end-to-end model development, from data preprocessing to deployment, ensuring seamless integration with client application platforms
Stay up to date with the latest developments in AI/ML and continuously improve your technical expertise to contribute to cutting-edge projects
Collaborate closely with cross-functional teams, including software engineers, and product managers, and leadership/exec-level stakeholders, to deliver impactful AI-driven solutions
Required Skills & Qualifications Education: Bachelors or Masters degree in Computer Science, Data Science, Machine Learning, or related field
Proficiency in Python (NumPy, Pandas, scikit-learn, TensorFlow/PyTorch)
Strong understanding of supervised & unsupervised learning techniques
Experience with time-series forecasting, classification, regression, and clustering
Solid understanding of statistical methods and probability
Familiarity with SQL and database querying
Knowledge of ML model deployment via APIs (Flask, FastAPI) or serving frameworks
Good to Have Exposure to financial datasets or fintech use cases
Experience with fraud detection, or financial forecasting
Cloud platform experience (AWS, Azure, GCP) for ML workloads
Understanding of data security and compliance (GDPR, PCI DSS)

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