ML/AI Engineer - Classical ML

Jobgether SRL; India; Remote; INR 2,800,000 - 4,000,000; Full time

Benefits offered by this job

100% remote work: Work from anywhere

Flexible working hours

Full-time employment

Udemy access for continuous learning

Certifications and training programs

Career progression opportunities

Inclusive, collaborative environment

Autonomy and ownership

Referral rewards

Well-being programs

Job summary

Partner Company in India is seeking an ML/AI Engineer focused on classical ML to transform ML solutions into production-ready systems. You will collaborate with Data Science teams to operationalize models, build robust data processing pipelines, and improve AI-driven applications in a fully remote setting.

You will deploy models, design scalable workflows, and implement ML lifecycle practices, MLOps, and LLMOps while leveraging Databricks, Spark, Hive, and cloud platforms.

Qualifications

• At least 5 years of data engineering experience, including the last 3 focused on data processing solutions.
• Hands-on experience building and deploying production-grade ML recommendation systems.
• At least 5 years of production-ready Python code, including microservices or APIs.
• 3+ years developing production ML applications.
• Practical experience with MLOps/LLMOps tools on Azure or Google Cloud Vertex AI.
• Databricks exposure and familiarity with Spark, Hive, PySpark in large-scale environments.
• Strong ML/AI fundamentals, lifecycle management, and model performance metrics.
• Cloud knowledge with Azure or Google Cloud; data warehousing or data lakes experience.
• Excellent communication and collaboration; fluent English.

Responsibilities

• Collaborate with Data Science teams to deploy and maintain ML models in production.
• Develop ML/AI and LLM automation solutions to improve scalability and efficiency.
• Design and manage production-grade data pipelines and ML workflows.
• Define best practices for ML lifecycle, MLOps, and LLMOps with reliable deployment.
• Support AI engineering frameworks and tooling adoption across teams.
• Research modern ML techniques and tools and apply them to projects.
• Gather requirements, estimate workloads, and contribute to planning and delivery.
• Present solutions to stakeholders and document technical details for knowledge sharing.
• Collaborate with cross-functional teams to improve engineering practices.

Skills

Python

MLOps

LLMOps

Databricks

Spark

Hive

APIs

Cloud platforms

Tools

Azure Machine Learning

Azure AI

Google Vertex AI

Databricks

PySpark

Job description

This position is listed on behalf of a partner company, which manages all applications and next steps. Our partner is looking for a ML/AI Engineer – Classical ML based in India.

In this role, you will help transform machine learning solutions into reliable, scalable, production-ready systems. You will collaborate with Data Science teams to operationalize machine learning models, build robust data processing pipelines, and improve the efficiency of AI-driven applications. Your work will contribute to the development of production recommendation systems and the implementation of modern MLOps and LLMOps practices. You will leverage cloud technologies, big data platforms, and machine learning frameworks to deliver innovative technical solutions. Working alongside experienced engineers and technical experts, you will help establish best practices across the machine learning lifecycle. This fully remote opportunity offers the autonomy, flexibility, and continuous learning needed to make a meaningful impact in a collaborative, international environment.

Accountabilities

• Collaborate with Data Science teams to deploy, integrate, and maintain machine learning models in production environments.
• Develop practical and innovative ML, AI, and LLM automation solutions that improve scalability, operational efficiency, and performance.
• Design, implement, and manage industrialized data processing pipelines and production-ready machine learning workflows.
• Define and implement best practices for the machine learning lifecycle, MLOps, and LLMOps, ensuring reliable model deployment, monitoring, and maintenance.
• Implement and support AI engineering, MLOps, and LLMOps frameworks, helping Data Science teams adopt effective tools, processes, and standards.
• Research and apply modern techniques, tools, and frameworks for machine learning architecture and operations.
• Gather technical requirements, estimate workloads, and contribute to planning and delivery activities.
• Present technical solutions, concepts, and results to internal and external stakeholders, communicating complex topics clearly.
• Create and maintain technical documentation to support knowledge sharing, operational consistency, and long-term maintainability.
• Collaborate with cross-functional teams to solve technical challenges, share expertise, and continuously improve engineering practices.

Requirements

• At least 5 years of data engineering experience, including the last 3 years focused on building and maintaining data processing solutions.
• Hands-on experience developing and deploying production-grade machine learning recommendation systems.
• At least 5 years of experience writing production-ready Python code, including microservices, APIs, or similar applications.
• At least 3 years of experience developing production-ready code for machine learning applications.
• Practical experience with MLOps and LLMOps tools and platforms, such as Azure Machine Learning, Azure AI, or Google Cloud Vertex AI.
• Hands-on experience with Databricks and familiarity with big data processing technologies, including Spark, PySpark, and Hive, in environments such as Databricks, Amazon EMR, or equivalent platforms.
• Strong understanding of machine learning and AI concepts, including algorithm types, ML frameworks, model performance and efficiency metrics, model lifecycle management, and AI architectures.
• Good understanding of cloud computing concepts and architectures, with practical knowledge of cloud services, preferably on Microsoft Azure or Google Cloud Platform.
• Experience in at least one of the following areas: data warehousing, data lakes, data integration, data governance, machine learning, deep learning, or MLOps.
• Proven experience designing, implementing, and maintaining data pipelines and scalable data processing solutions.
• Strong problem-solving and critical-thinking skills, with the ability to analyze complex technical challenges and develop effective solutions.
• Excellent communication and collaboration skills, with the ability to work effectively in a team, support colleagues, and take ownership of assigned tasks and deliverables.
• Fluency in written and spoken English.
• A proactive mindset, willingness to learn, and motivation to contribute to a collaborative, knowledge-sharing environment.

Benefits

• 100% remote work: Work from anywhere in India with the flexibility to organize your working environment.
• Flexible working hours: Enjoy greater autonomy in managing your schedule and work-life balance.
• Full-time, stable employment: Join an established international organization with a long-standing presence in the data and technology services market.
• Structured onboarding: Benefit from a comprehensive online onboarding program and support from a dedicated buddy from your first day.
• Expert collaboration: Work alongside experienced engineers, technical specialists, and data science professionals on challenging projects.
• Continuous learning: Access the Udemy learning platform from day one to strengthen your technical and professional skills.
• Professional certifications: Benefit from certificate training programs and opportunities to validate your technical expertise.
• Upskilling and development: Participate in capability development programs, Competency Centers, knowledge-sharing sessions, community webinars, and a wide range of training opportunities.
• Career progression: Explore internal mobility and advancement opportunities within a growing international organization.
• Inclusive work environment: Become part of a diverse, supportive, and values-driven professional community.
• Autonomy and ownership: Have the freedom to contribute ideas, take responsibility, and influence how you approach your work.
• Referral rewards: Earn bonuses by recommending qualified professionals to join the organization.
• Well-being initiatives: Access activities and initiatives designed to support your health and well-being.
• Social and environmental impact: Participate in charitable initiatives and activities supporting environmental sustainability.