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.