We are seeking a Machine Learning Engineer with strong data analytics skills to help drive business value through intelligent, data-driven solutions. The ideal candidate will be proficient in analyzing large datasets, developing scalable machine learning models tailored to business use cases, and deploying them using AWS cloud infrastructure. A strong understanding of when to apply traditional ML models versus Large Language Models (LLMs) is essential.

Key Responsibilities:

Analyze high-volume, complex datasets to identify trends, patterns, and business opportunities.

Design, develop, and deploy ML models and LLMs to solve real-world business problems.

Evaluate and select between LLMs and traditional ML models based on use case fit.

Build and optimize data pipelines for feature engineering and model training.

Deploy models into production using AWS services such as SageMaker, Lambda, EC2, and S3.

Monitor and maintain model performance, including retraining and scalability improvements.

Communicate data insights and model results to both technical and non-technical stakeholders.

Collaborate closely with data engineers, analysts, product managers, and domain experts.

Mandatory Skills:

Machine Learning: Model development, training, tuning, and evaluation using standard ML algorithms (e.g., regression, classification, clustering).

LLM vs ML Selection: Ability to choose between LLMs and traditional ML approaches based on use cases.

Programming: Proficiency in Python and ML libraries such as scikit-learn, Pandas, NumPy, TensorFlow, or PyTorch.

Cloud Deployment (AWS): Experience with AWS SageMaker, Lambda, EC2, and S3 for scalable model deployment.

Data Analysis: Expertise in exploratory data analysis (EDA), statistical analysis, and working with large datasets.

SQL: Strong command of SQL for querying and manipulating structured data.

Model Monitoring & Automation: Experience in deploying, monitoring, and automating ML pipelines in production.

Communication: Ability to translate complex ML solutions into business-friendly language.

Good to Have Skills:

LLM Tools: Experience with frameworks like Hugging Face Transformers or similar.

Data Pipeline Optimization: Familiarity with feature engineering best practices and ETL workflows.

CI/CD for ML: Exposure to MLOps practices and tools (e.g., MLflow, Airflow, or Kubeflow).

Domain Knowledge: Understanding of how ML solutions can drive business metrics in domains such as finance, marketing, or operations.

Visualization: Proficiency in using visualization tools like Matplotlib, Seaborn, or Plotly.