Machine Learning Engineer - Data Modeling

Role : Machine Learning Engineer

Location : Work From Home

Experience : 2-5 Years

Employment Type : Full-time

Job Overview :

We're seeking a highly skilled Machine Learning Engineer with 2-5 years of hands-on experience to join our remote team. You will be at the forefront of leveraging advanced AI and machine learning techniques to build impactful, production-ready solutions that are core to our business operations. This role involves end-to-end model development, deployment, and optimization for critical areas such as credit decisions, customer service automation using LLM/NLP, and robust fraud prevention. If you possess strong software engineering fundamentals, expertise in Python and SQL, a deep understanding of cloud infrastructure, and a passion for driving measurable business impact, we encourage you to apply.

Key Responsibilities

Credit Decisions & Risk Modeling :

- Design, develop, and deploy predictive machine learning models to assess creditworthiness, building and refining signals from diverse unstructured (e.g., text, logs) and structured data sources.

- Implement feature engineering pipelines, model training, validation, and continuous monitoring strategies to ensure model accuracy, fairness, and robustness in production.

- Utilize advanced statistical methods and machine learning algorithms (e.g., gradient boosting, deep learning) to identify and quantify risk.

Customer Service Automation (LLM/NLP) :

- Research, prototype, and implement Natural Language Processing (NLP) and Large Language Model (LLM)-based solutions to automate customer service interactions.

- Develop components for intent recognition, sentiment analysis, named entity recognition, and response generation, providing real-time context and recommendations to our customer service team.

- Experience with fine-tuning pre-trained LLMs or leveraging APIs from providers like OpenAI, Hugging Face, or similar is highly desirable.

Fraud Prevention & Anomaly Detection :

- Develop and deploy sophisticated machine learning models for fraud detection and prevention, identifying complex patterns of anomalous and malicious behavior from transactional and behavioral data.

- Implement real-time inference pipelines and integrate with existing systems to enable immediate fraud alerts and mitigation strategies.

- Apply techniques such as unsupervised learning for anomaly detection and graph-based models for relational fraud patterns.

MLOps & Infrastructure :

- Build, optimize, and maintain scalable data pipelines (ETL/ELT) for feature generation and model training data using Python and SQL.

- Deploy and manage ML models in a production cloud computing infrastructure environment (e.g., AWS, GCP, Azure), ensuring high availability, low latency, and efficient resource utilization.

- Implement MLOps best practices for model versioning, continuous integration/continuous delivery (CI/CD) of ML pipelines, model monitoring, and retraining strategies.

Cross-functional Collaboration & Technical Leadership :

- Bring your experience to bear on the technical direction and capabilities of the team, contributing to architectural decisions and fostering a culture of technical excellence.

- Work cross-functionally with product managers, policy teams, and other engineering disciplines to translate business requirements into technical specifications and deliver impactful solutions.

- Actively participate in code reviews, design discussions, and knowledge sharing to elevate the overall team's proficiency.

Required Technical Skills & Qualifications

- 2-5 years of hands-on experience building, deploying, and maintaining machine learning solutions in a production software environment.

- Excellent software engineering and programming skills, with expert proficiency in Python (including libraries like Pandas, NumPy, scikit-learn, TensorFlow/PyTorch).

- Expert-level proficiency in SQL for complex data extraction, manipulation, and analysis from relational and non-relational databases.

- A diverse range of data skills, including :

- Strong understanding and practical experience with experimental design, A/B testing, and statistical analysis.

- Solid theoretical and practical knowledge of various machine learning algorithms (e.g., regression, classification, clustering, ensemble methods).

- Experience with feature engineering, model selection, hyperparameter tuning, and model evaluation metrics.

- Deep understanding of using cloud computing infrastructure (e.g., AWS S3, EC2, SageMaker, Lambda; Azure ML, Data Lake; GCP AI Platform, BigQuery) and building robust data pipelines (e.g., Airflow, Data Factory) in production.

- Experience with version control systems (Git) and collaborative development workflows.

Preferred Qualifications

- Experience with LLMs, NLP techniques, and frameworks (e.g., spaCy, NLTK, Hugging Face Transformers).

- Familiarity with containerization technologies (e.g., Docker) and orchestration (e.g., Kubernetes).

- Experience in a startup or early-stage team environment, demonstrating adaptability and a proactive approach.

Candidate Attributes

- Self-motivation : You teach yourself new skills and stay current with the rapidly evolving AI/ML landscape. You take the initiative to anticipate and solve problems before they arise. You are pragmatic and focused on delivering tangible results.

- Team motivation : You are an active listener, articulate your ideas clearly and concisely, and ask insightful questions to foster understanding. You are a highly collaborative team player and enjoy sharing knowledge and mentoring others.

- The drive to make a positive impact on customers' lives through innovative technology solutions.