Sr. ML Engineer

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Sr. ML Engineer based in India.

This role offers the opportunity to design, build, and deploy advanced machine learning solutions that create measurable business impact. You will work across the complete ML lifecycle, from data acquisition and model development to production deployment and monitoring. The position combines hands-on engineering with strategic problem-solving, enabling you to influence how AI solutions are built and scaled. You will collaborate with data scientists, engineers, and architects in a highly technical environment focused on innovation and automation. The role requires strong expertise in machine learning, cloud platforms, and MLOps practices to deliver reliable, production-ready systems. You will contribute to improving data-driven decision-making through scalable AI solutions and modern engineering practices.

Accountabilities:: The Sr. ML Engineer will be responsible for developing, deploying, and optimizing machine learning solutions while ensuring scalability, reliability, and performance across the ML lifecycle. Key responsibilities include:

Managing the complete machine learning lifecycle, including data acquisition, data cleaning, model development, deployment, and monitoring.

Translating business objectives into effective ML solutions and defining relevant metrics to measure performance and success.

Building and deploying machine learning and deep learning models across areas such as predictive analytics, time series, NLP, and computer vision.

Evaluating and selecting appropriate algorithms, validation strategies, preprocessing techniques, and feature engineering approaches.

Exploring and analyzing datasets to identify patterns, improve model performance, and address data quality challenges.

Training models, tuning hyperparameters, analyzing errors, and implementing strategies to improve accuracy and reliability.

Designing and architecting scalable data pipelines across on-premise, cloud, and hybrid environments.

Developing and maintaining MLOps workflows, including model versioning, testing, deployment, monitoring, and optimization.

Implementing cloud-based AI solutions and building MLOps capabilities using Azure services.

Working with workflow orchestration tools such as Kubeflow, Airflow, Argo, or similar platforms.

Automating existing processes, improving engineering workflows, and introducing innovative approaches to data management and validation.

Ensuring high-quality software development practices through unit testing, integration testing, code optimization, and refactoring.

Collaborating with cross-functional teams including data scientists, data engineers, and architects while documenting technical processes.

Requirements:

The ideal candidate is an experienced machine learning professional with strong software engineering foundations and expertise in designing production-grade AI systems. Required qualifications and skills include:

4–7 years of experience in data science, including 3+ years working as an ML Engineer.

5+ years of hands-on experience developing object-oriented applications using Python.

Strong knowledge of machine learning frameworks, libraries, data structures, data modeling, and software architecture.

Solid understanding of mathematics, statistics, algorithms, and machine learning principles.

Experience building, deploying, and managing machine learning workflows using Docker containers and Kubernetes.

Hands-on experience with ML frameworks such as TensorFlow, Caffe, PyTorch, or Keras.

Experience designing and implementing solutions using Azure cloud services, including compute environments, Cosmos DB, Streaming Analytics, IoT messaging capabilities, and Azure Functions.

Understanding of data systems, scalable architectures, and production software engineering practices.

Experience with deep learning approaches and modern AI modeling techniques.

Strong analytical thinking, problem-solving abilities, and the ability to collaborate effectively with technical teams.

Benefits:

Competitive salary package aligned with experience and technical expertise.

Opportunity to work on impactful machine learning projects using modern AI technologies.

Exposure to cloud platforms, MLOps practices, and scalable production systems.

Collaborative environment with experienced data scientists, engineers, and architects.

Opportunities to innovate, improve processes, and contribute to advanced AI solutions.

Flexible and technology-driven work environment.

How Jobgether works: We use an AI-powered matching process to ensure your application is reviewed quickly, objectively, and fairly against the role's core requirements. Our system identifies the top-fitting candidates, and this shortlist is then shared directly with the hiring company. The final decision and next steps (interviews, assessments) are managed by their internal team. We appreciate your interest and wish you the best!  Why Apply Through Jobgether?    Data Privacy Notice: By submitting your application, you acknowledge that Jobgether will process your personal data to evaluate your candidacy and share relevant information with the hiring employer. This processing is based on legitimate interest and pre-contractual measures under applicable data protection laws (including GDPR). You may exercise your rights (access, rectification, erasure, objection) at any time.     #LI-CL1