Machine Learning Engineer

Role - ML Engineer

Experience - 3+Yrs

Location - Bangalore

What You Will Achieve and Key Responsibilities

AI System Development

Design and develop Computer Vision, NLP, and Recommendation Systems for our applications.

Research and implement novel deep learning architectures to solve complex data science problems.

Develop, train, and deploy deep learning and machine learning models that are scalable and extensible.

Full Product Lifecycle Participation

Collaborate with product managers, UX designers, and end-users to integrate software and AI components into fully functional systems.

Participate in the complete product lifecyclefrom concept design to development, integration, testing, and deployment.

Scalable Solutions

Build products that handle large data volumes efficiently while remaining highly scalable for onboarding new clients.

Design complete end-to-end data pipelines and ML pipelines for seamlessintegration into production environments.

Research & Collaboration

Work closely with the leadership team on research and development efforts to explore cutting-edge technologies.

Uphold our culture of engineering excellence by maintaining high standards in code quality and innovation.

Why This Matters

Your contributions will be instrumental in advancing Parspecs AI capabilities, enabling

us to build intelligent systems that solve real-world problems in construction technology.

By developing scalable AI solutions, you will help digitize an industry while driving

innovation through state-of-the-art machine learning techniques.

Who You Are

You are a motivated Machine Learning Engineer with at least 3 years of relevant

experience who is passionate about working on innovative projects in a dynamic

environment. You thrive on solving challenging problems using advanced AI

technologies.

Minimum Qualifications

Bachelors or Masters degree in Science or Engineering with strong programming, critical thinking, and analytical skills.

Strong conceptual understanding of machine learning and deep learning principles.

Expertise in Computer Vision or Natural Language Processing (NLP).

Hands-on experience implementing ML projects in Python using libraries like NumPy, scikit-learn, matplotlib, pandas, etc.

Proficiency in training deep learning models using TensorFlow/Keras or PyTorch.

Ability to write clean, efficient, and bug-free code.

Proven ability to lead initiatives from concept to operation while navigating challenges effectively.