Job Title : Senior Machine Learning Engineer Applied AI

The candidate should be :

- A strong software engineer with deep ML expertise

- A pragmatic data scientist

- Skilled in building robust, scalable ML applications

- Able to bridge ML research and production-ready systems

Key Responsibilities :

- End-to-End ML Application Development : Design, develop, deploy ML models/systems into production (robust, scalable, high-performing)

- Software Design & Architecture : Create clean, modular, testable ML pipelines, APIs, services; make architectural decisions

- ML Model Development & Optimization : Collaborate for business understanding, explore data, build/train/evaluate ML models (supervised/unsupervised/deep/reinforcement learning), optimize models

- Data Engineering for ML : Build data pipelines for feature engineering, data handling/versioning

- MLOps & Productionization : Implement best practices (CICD, auto-testing, model versioning/monitoring, alerting for drift/bias)

- Performance & Scalability : Diagnose/fix bottlenecks, make models scalable/reliable

- Collaboration/Mentorship : Work with cross-functional teams (data scientists, SW engineers, PMs, DevOps), possibly mentor juniors

- Research/Innovation : Stay updated on ML/MLOps tech, suggest/explore improvements

- Documentation : Write clear docs for ML models, pipelines, services

Required Qualifications :

- Education : Bachelors or Masters in CS, ML, Data Science, EE, or similar

- Experience : 5+ years professional ML/swe engineering (strong ML focus)

- Programming : Expert-level Python (clean, efficient code); other languages (Java, Go, C) a bonus

- Software Fundamentals : Patterns, structures, algorithms, OOP, distributed systems

- ML Expertise : Theory/practice with ML algorithms; frameworks (PyTorch, Scikit-learn); feature engineering; metrics; tuning

- Data Handling : SQL/NoSQL databases; handling big datasets

- Problem-Solving : Strong analytical/pragmatic approach

- Communication : Clear technical/non-technical explanation skills

Preferred Qualifications :

- Masters/PhD in relevant field

- Big data (Spark, Hadoop, Kafka) experience

- Open-source/personal project portfolio

- AB testing, experimental design for ML

- Data governance, privacy, security knowledge in ML

This role requires someone strong technically, and capable of owning ML products end-to-end while working across teams and mentoring junior engineers. The candidate needs both research and practical production experience in ML systems.