ML/AI Engineer - Classical ML

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.