Machine Learning Engineer

Machine Learning Engineer

Responsibilities

• Build, test, and deploy machine learning models for various data-driven use cases
• Develop scalable data preprocessing and feature engineering pipelines
• Collaborate with product and engineering teams to integrate AI functionalities into production systems
• Analyze model performance, run experiments, and fine-tune algorithms for accuracy and speed
• Contribute to maintaining version-controlled ML workflows and reusable modules
• Assist in preparing training datasets from structured and unstructured sources
• Support model monitoring, feedback loop integration, and continuous improvements

Technical Skills Required

• Strong foundation in Python and applied machine learning
• Experience with one or more ML frameworks such as scikit-learn, TensorFlow, or PyTorch
• Understanding of supervised, unsupervised, and basic NLP techniques
• Familiarity with building data pipelines and working with large datasets (Pandas, NumPy, SQL)
• Exposure to model deployment concepts, APIs, and inference
• Basic knowledge of Git, Docker, and cloud environments (AWS/GCP/Azure)

Preferred (Nice to Have)

• Experience with document data (PDFs, scanned files) or OCR tools
• Exposure to transformer-based models or LLMs
• Familiarity with ML lifecycle tools (MLflow, DVC, Weights & Biases)
• Prior experience in fintech, risk analysis, or fraud detection systems

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, or a related field
• 3+ years of hands-on experience in ML/AI development roles

Compensation & Growth

• Competitive compensation aligned with industry benchmarks
• ESOPs and performance bonuses for high performers
• Exposure to real-world AI use cases with domain experts in fintech, compliance, and GenAI
• Opportunity to grow into senior research or MLOps roles based on performance and interest