Sr Machine Learning Engineer

Sr Machine Learning Engineer at TrumetricAI · Bengaluru (Bangalore) · 6 - 10 years · ₹15L - ₹30L / yr · Bootstrapped · Posted 6 Jan 2026

Sr Machine Learning Engineer

at TrumetricAI

Posted by Yashika Tiwari

6 - 10 yrs

₹15L - ₹30L / yr

Bengaluru (Bangalore)

Skills

Natural Language Processing (NLP)

Deep Learning

Artificial Intelligence (AI)

Generative AI

Machine Learning (ML)

Data Science

Senior Machine Learning Engineer

About the Role

We are looking for a Senior Machine Learning Engineer who can take business problems, design appropriate machine learning solutions, and make them work reliably in production environments.

This role is ideal for someone who not only understands machine learning models, but also knows when and how ML should be applied, what trade-offs to make, and how to take ownership from problem understanding to production deployment.

Beyond technical skills, we need someone who can lead a team of ML Engineers, design end-to-end ML solutions, and clearly communicate decisions and outcomes to both engineering teams and business stakeholders. If you enjoy solving real problems, making pragmatic decisions, and owning outcomes from idea to deployment, this role is for you.

What You’ll Be Doing

Building and Deploying ML Models

Design, build, evaluate, deploy, and monitor machine learning models for real production use cases.
Take ownership of how a problem is approached, including deciding whether ML is the right solution and what type of ML approach fits the problem.
Ensure scalability, reliability, and efficiency of ML pipelines across cloud and on-prem environments.
Work with data engineers to design and validate data pipelines that feed ML systems.
Optimize solutions for accuracy, performance, cost, and maintainability, not just model metrics.

Leading and Architecting ML Solutions

Lead a team of ML Engineers, providing technical direction, mentorship, and review of ML approaches.
Architect ML solutions that integrate seamlessly with business applications and existing systems.
Ensure models and solutions are explainable, auditable, and aligned with business goals.
Drive best practices in MLOps, including CI/CD, model monitoring, retraining strategies, and operational readiness.
Set clear standards for how ML problems are framed, solved, and delivered within the team.

Collaborating and Communicating

Work closely with business stakeholders to understand problem statements, constraints, and success criteria.
Translate business problems into clear ML objectives, inputs, and expected outputs.
Collaborate with software engineers, data engineers, platform engineers, and product managers to integrate ML solutions into production systems.
Present ML decisions, trade-offs, and outcomes to non-technical stakeholders in a simple and understandable way.

What We’re Looking For

Machine Learning Expertise

Strong understanding of supervised and unsupervised learning, deep learning, NLP techniques, and large language models (LLMs).
Experience choosing appropriate modeling approaches based on the problem, available data, and business constraints.
Experience training, fine-tuning, and deploying ML and LLM models for real-world use cases.
Proficiency in common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.

Production and Cloud Deployment

Hands-on experience deploying and running ML systems in production environments on AWS, GCP, or Azure.
Good understanding of MLOps practices, including CI/CD for ML models, monitoring, and retraining workflows.
Experience with Docker, Kubernetes, or serverless architectures is a plus.
Ability to think beyond deployment and consider operational reliability and long-term maintenance.

Data Handling

Strong programming skills in Python.
Proficiency in SQL and working with large-scale datasets.
Ability to reason about data quality, data limitations, and how they impact ML outcomes.
Familiarity with distributed computing frameworks like Spark or Dask is a plus.

Leadership and Communication

Ability to lead and mentor ML Engineers and work effectively across teams.
Strong communication skills to explain ML concepts, decisions, and limitations to business teams.
Comfortable taking ownership and making decisions in ambiguous problem spaces.
Passion for staying updated with advancements in ML and AI, with a practical mindset toward adoption.

Experience Needed

6+ years of experience in machine learning engineering or related roles.
Proven experience designing, selecting, and deploying ML solutions used in production.
Experience managing ML systems after deployment, including monitoring and iteration.
Proven track record of working in cross-functional teams and leading ML initiatives.

Senior Machine Learning Engineer

About the Role

What You’ll Be Doing

Building and Deploying ML Models

Design, build, evaluate, deploy, and monitor machine learning models for real production use cases.
Take ownership of how a problem is approached, including deciding whether ML is the right solution and what type of ML approach fits the problem.
Ensure scalability, reliability, and efficiency of ML pipelines across cloud and on-prem environments.
Work with data engineers to design and validate data pipelines that feed ML systems.
Optimize solutions for accuracy, performance, cost, and maintainability, not just model metrics.

Leading and Architecting ML Solutions

Lead a team of ML Engineers, providing technical direction, mentorship, and review of ML approaches.
Architect ML solutions that integrate seamlessly with business applications and existing systems.
Ensure models and solutions are explainable, auditable, and aligned with business goals.
Drive best practices in MLOps, including CI/CD, model monitoring, retraining strategies, and operational readiness.
Set clear standards for how ML problems are framed, solved, and delivered within the team.

Collaborating and Communicating

Work closely with business stakeholders to understand problem statements, constraints, and success criteria.
Translate business problems into clear ML objectives, inputs, and expected outputs.
Collaborate with software engineers, data engineers, platform engineers, and product managers to integrate ML solutions into production systems.
Present ML decisions, trade-offs, and outcomes to non-technical stakeholders in a simple and understandable way.

What We’re Looking For

Machine Learning Expertise

Strong understanding of supervised and unsupervised learning, deep learning, NLP techniques, and large language models (LLMs).
Experience choosing appropriate modeling approaches based on the problem, available data, and business constraints.
Experience training, fine-tuning, and deploying ML and LLM models for real-world use cases.
Proficiency in common ML frameworks such as TensorFlow, PyTorch, Scikit-learn, etc.

Production and Cloud Deployment

Hands-on experience deploying and running ML systems in production environments on AWS, GCP, or Azure.
Good understanding of MLOps practices, including CI/CD for ML models, monitoring, and retraining workflows.
Experience with Docker, Kubernetes, or serverless architectures is a plus.
Ability to think beyond deployment and consider operational reliability and long-term maintenance.

Data Handling

Strong programming skills in Python.
Proficiency in SQL and working with large-scale datasets.
Ability to reason about data quality, data limitations, and how they impact ML outcomes.
Familiarity with distributed computing frameworks like Spark or Dask is a plus.

Leadership and Communication

Ability to lead and mentor ML Engineers and work effectively across teams.
Strong communication skills to explain ML concepts, decisions, and limitations to business teams.
Comfortable taking ownership and making decisions in ambiguous problem spaces.
Passion for staying updated with advancements in ML and AI, with a practical mindset toward adoption.

Experience Needed

6+ years of experience in machine learning engineering or related roles.
Proven experience designing, selecting, and deploying ML solutions used in production.
Experience managing ML systems after deployment, including monitoring and iteration.
Proven track record of working in cross-functional teams and leading ML initiatives.

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About TrumetricAI

Founded :

2025

Type :

Services

Size :

0-20

Stage :

Bootstrapped

About

What if your data could think for you? TruMetric by Specbee blends AI and data analytics to drive smarter decisions, higher ROI, and faster growth.

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• Optimize model performance, scalability, and reliability for production environments.

• Implement MLOps best practices using tools such as MLflow, Docker, Kubernetes, and Kubeflow.

• Stay updated with emerging trends and research in AI, ML, Deep Learning, and Generative AI.

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