Job Description: 

Position Title: Data Scientist/Machine Learning Engineer

Experience: - Minimum 3 Years
Location: Remote
Employment Type: Full-Time with Rulesiq

Role Summary
We are seeking a highly skilled and versatile Data Scientist/Machine Learning Engineer to join our team. The ideal candidate will have a strong foundation in machine learning, data science, and software engineering, coupled with the ability to design and implement end-to-end systems. This role involves working with cutting-edge technologies, including LLMs, recommendation models, and NLP, while leveraging big data engineering, system design, and cloud infrastructure to deliver impactful solutions.

Key Responsibilities

1. Machine Learning & Data Science
   • Develop and deploy machine learning models for various use cases such as recommendation systems, propensity scoring, and NLP.
   • Design, train, and fine-tune large language models (LLMs) and integrate them into production workflows.
   • Conduct exploratory data analysis (EDA), feature engineering, and statistical modeling to derive actionable insights.
2. Big Data Engineering
   • Build and optimize data pipelines and workflows for large-scale data processing using tools like Apache Spark, EMR, or similar.
   • Collaborate with the data engineering team to ensure data integrity, scalability, and efficiency.
3. System Design & Development
   • Architect and implement end-to-end ML systems, from data ingestion to model deployment and monitoring.
   • Develop robust and scalable APIs for model integration and data access.
   • Ensure seamless integration with backend systems (MongoDB) and cloud infrastructure (AWS).
4. Infrastructure & DevOps
   • Containerize applications and ML models using Docker, ensuring portability and consistency across environments.
   • Orchestrate and manage deployments using Kubernetes.
   • Monitor and optimize system performance, ensuring high availability and reliability.
5. Cloud Computing & Database Management
   • Utilize AWS services such as S3, Lambda, SageMaker, and ECS for building and deploying solutions.
   • Design efficient and scalable data storage solutions using MongoDB and related tools.
6. Collaboration & Communication
   • Work closely with cross-functional teams, including data engineers, software developers, and product managers.
   • Translate business requirements into technical solutions.

Requirements

Technical Skills

• Proficiency in Python, with expertise in libraries such as NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers.
• Strong understanding of machine learning algorithms, deep learning architectures, and NLP techniques.
• Hands-on experience with recommendation systems, propensity scoring, and statistical methods.
• Knowledge of big data tools (e.g., Spark, Hadoop) and stream processing.
• Solid experience with API development and integration.
• Expertise in Docker, Kubernetes, and CI/CD practices.
• Familiarity with AWS services and cloud-native architectures.

Analytical & Design Skills

• Strong grasp of data science concepts, including predictive modeling, clustering, and classification.
• Experience with LLM fine-tuning and deployment for NLP applications.
• Sound understanding of system design principles and infrastructure best practices.

Education & Experience

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• 3+  (3-5)years of professional experience in machine learning engineering or data science roles.
• Previous experience in building and deploying end-to-end ML pipelines in production environments.

Nice-to-Have Skills

• Experience with MongoDB Atlas and serverless architectures.
• Knowledge of MLOps tools and practices for productionizing ML models.
• Familiarity with monitoring and observability tools (e.g., Prometheus, Grafana).