We are seeking a talented Machine Learning Engineer to join our innovative technology team. The ideal candidate will have a strong background in artificial intelligence, machine learning, data engineering, and cloud technologies. You will be responsible for designing, developing, deploying, and maintaining scalable machine learning solutions that solve complex business challenges and deliver measurable business value.

This role requires close collaboration with Data Scientists, Software Engineers, Product Managers, and DevOps teams to build production-grade AI applications.

Key Responsibilities

Design, develop, and deploy scalable machine learning models for production environments.

Build predictive analytics, recommendation engines, classification, regression, clustering, and forecasting models.

Develop end-to-end ML pipelines including data collection, preprocessing, feature engineering, model training, validation, deployment, and monitoring.

Optimize machine learning algorithms for performance, scalability, and accuracy.

Implement deep learning models using TensorFlow, PyTorch, or similar frameworks.

Build NLP and Generative AI applications using Large Language Models (LLMs).

Develop Retrieval-Augmented Generation (RAG) pipelines for enterprise AI solutions.

Deploy ML models using Docker, Kubernetes, MLflow, and cloud platforms.

Collaborate with cross-functional teams to understand business requirements and translate them into AI-driven solutions.

Perform model evaluation, hyperparameter tuning, A/B testing, and continuous performance monitoring.

Develop APIs and microservices to serve machine learning models.

Maintain documentation, coding standards, and version control.

Research emerging AI technologies and recommend innovative solutions.

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Required Qualifications

Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, Mathematics, Statistics, or a related discipline.

3+ years of hands-on experience developing and deploying machine learning models.

Strong programming skills in Python.

Experience building production-ready machine learning applications.

Solid understanding of supervised, unsupervised, reinforcement, and deep learning techniques.

Strong knowledge of probability, statistics, and linear algebra.

Experience working with large structured and unstructured datasets.

Excellent analytical and problem-solving skills.

Strong communication and collaboration abilities.

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Technical Skills

Programming Languages

Python

SQL

Java (Preferred)

Scala (Preferred)

Machine Learning Libraries

Scikit-learn

TensorFlow

PyTorch

XGBoost

LightGBM

CatBoost

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Machine Learning Engineer