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Machine Learning Engineer (Mid/Senior) (Remote, Full-Time) [AS221]

Smart Working Solutions

India Remote permanent

Posted: March 20, 2026

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Quick Summary

A Machine Learning Engineer is responsible for designing and developing machine learning models to drive business growth.

Job Description

About Smart Working

At Smart Working, we believe your job should not only look right on paper but also feel right every day. This isn’t just another remote opportunity - it’s about finding where you truly belong, no matter where you are. From day one, you’re welcomed into a genuine community that values your growth and well-being.

Our mission is simple: to break down geographic barriers and connect skilled professionals with outstanding global teams and products for full-time, long-term roles. We help you discover meaningful work with teams that invest in your success, where you’re empowered to grow personally and professionally.
Join one of the highest-rated workplaces on Glassdoor and experience what it means to thrive in a truly remote-first world.

About the Role

We are looking for a Machine Learning Engineer to drive the development and integration of AI capabilities across the business. This role focuses on building end-to-end machine learning solutions, from data extraction and modelling through to deployment and integration into internal systems.

You will play a key role in automating complex processes, working closely with stakeholders to translate real-world business problems into scalable ML solutions. You will also help introduce machine learning best practices and shape the organisation’s growing AI capability.
The role is primarily backend-focused, with some exposure to data visualisation or light frontend work where needed.
The initial project will focus on automating the report writing process, leveraging data from site investigations and insurance policies, and training models on large volumes of historical reports to generate accurate, context-aware technical outputs.


Responsibilities:
• Extract, clean, and structure data from internal systems

• Design and develop end-to-end ML pipelines (data ingestion → model → deployment)

• Train, evaluate, and optimise models to improve accuracy and business impact

• Integrate machine learning models into internal tools and existing systems

• Work with stakeholders to translate business problems into ML solutions

• Continuously monitor and improve model performance

• Introduce and promote ML best practices, standards, and tooling

• Contribute to building and scaling the organisation’s AI capability

• Collaborate with engineering teams to ensure scalable and reliable deployment of solutions


Requirements:
• 4+ years total experience (Mid–Senior level)

• Python – 3+ years (substitutes: other ML-focused languages for exceptional candidates)

• Machine Learning – 3+ years

• SQL / Databases – 3+ years (substitutes: other database technologies)

• .NET / C# – 1+ year

• Azure – 1+ year (substitutes: AWS)

• Experience building production-grade ML systems and pipelines

• Ability to work with large datasets (structured and unstructured)

• Strong problem-solving skills with a focus on real-world business impact

• Experience working closely with stakeholders and cross-functional teams

• Project ownership experience


Nice to Have:
• Experience in Data Engineering

• Exposure to cloud-based ML pipelines and data platforms

• Experience integrating ML solutions into existing business systems


Benefits:
• Fixed Shifts: 12:00 PM - 9:30 PM IST (Summer) | 1:00 PM - 10:30 PM IST (Winter)

• No Weekend Work: Real work-life balance, not just words

• Day 1 Benefits: Laptop and full medical insurance provided

• Support That Matters:Mentorship, community, and forums where ideas are shared

• True Belonging: A long-term career where your contributions are valued


At Smart Working, you’ll never be just another remote hire.

Be a Smart Worker - valued, empowered, and part of a culture that celebrates integrity, excellence, and ambition.

If that sounds like your kind of place, we’d love to hear your story.

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