AI/ML Engineer

3 weeks ago

Gurugram, India SMARTWORK IT SERVICES Full time

Job Title : AI ML Engineer (Hybrid)

Location : Gurgaon

Experience : 5+ Years

Job Description :

We are seeking a highly skilled and detail-oriented AI/ML Engineer with 5+ years of hands-on experience in building machine learning models, with a strong focus on anomaly detection and fraud detection.

The ideal candidate will have practical expertise in Isolation Forest and other anomaly detection algorithms, along with a solid understanding of model interpretability, hyperparameter tuning, and variable distribution analysis.

The role involves working closely with data science and product teams to design and deploy robust ML solutions that can detect irregular or suspicious patterns in data, especially for financial or transactional systems.

Key Responsibilities :

- Design and implement ML models for fraud and anomaly detection, using techniques like Isolation Forest, clustering, and statistical profiling.

- Work with large datasets to extract meaningful patterns and build predictive models.

- Conduct hyperparameter tuning and document tuning strategies and outcomes.

- Perform model interpretability analysis using SHAP, LIME, or similar frameworks, and prepare supporting documentation.

- Analyze variable distribution statistics and feature importance to refine model inputs.

- Collaborate with data engineers and product teams to integrate models into production pipelines.

- Continuously monitor model performance and refine as needed for accuracy, precision, and scalability.

Mandatory Skills :

- Strong experience in AI/ML model development (5+ years).

- Hands-on expertise in Isolation Forest and anomaly detection methods.

- Experience in fraud detection use cases or systems.

- Proficiency in Python and ML libraries (scikit-learn, pandas, NumPy, etc.)

- Knowledge of model tuning, interpretability tools, and data profiling.

- Excellent documentation skills for modeling workflows, parameters, and outcomes.

Preferred Skills :


- Experience with cloud platforms (AWS, Azure, GCP).

- Familiarity with big data tools (Spark, Hadoop).

- Exposure to real-time model deployment or streaming data environments.

- Understanding of risk and compliance in financial data systems.


(ref:hirist.tech)

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