Introduction
This article describes a machine learning engineer role that spans end-to-end model development, data pipeline management, experimentation, production deployment, and research. It outlines core responsibilities—feature engineering, model selection, monitoring—and the essential technical and educational requirements. Job details including work schedule, location, perks, ESOPs, and salary range are also summarized for a complete picture of the opportunity.
Role Responsibilities and Technical Workflow
The position centers on the full lifecycle of machine learning systems. At the outset you will design, develop and implement machine learning models, translating problem statements into model architectures and training routines. This development work is tightly coupled with building and maintaining data pipelines, ensuring reliable flow of training and inference data. Data pipelines support model development by enabling consistent data ingestion, preprocessing, and feature delivery.
Once models and pipelines are in place, the role demands rigorous iteration: conduct experiments and iterate on models. Experimentation includes systematic evaluation, tuning, and reworking of preprocessing, features, and model choice. To support reproducible experiments and collaborative tracking you will use MLflow and Weights & Biases, tools that record runs, artifacts, and metrics to compare variants and guide selection.
After validation, you will deploy and monitor models in production. Deployment implies moving validated models into an operational environment where models serve predictions, while monitoring ensures they continue to meet performance and reliability expectations. Continuous monitoring loops back into the experimental workflow: production insights feed subsequent iterations.
Complementing development and production activities is an explicit mandate to research new ML techniques and tools. This research informs choices around architectures, frameworks, and experimentation workflows, keeping the stack and methods current. Within model development, you will focus on feature engineering and model selection, applying domain understanding and systematic evaluation to choose features and algorithms that best address the task at hand.
The role also includes project-specific work: work on NLP and/or Computer Vision projects as needed. Depending on business needs, you will apply the above workflows—data pipelines, experimentation, deployment, monitoring—to natural language or visual tasks, adapting feature engineering and model selection accordingly.
Candidate Requirements, Work Structure and Compensation
Educationally, candidates should hold a Bachelor's or Master's in Computer Science, Data Science, Statistics, or a related field. The technical toolkit required includes Python and ML libraries such as TensorFlow, PyTorch, and Scikit-learn, enabling implementation of models, training loops, and evaluation scripts. An understanding of data preprocessing is essential for preparing raw inputs into model-ready features, and familiarity with distributed systems supports scalable data pipelines and production deployments.
Beyond technical knowledge, the role requires strong analytical and communication skills. Analytical skills drive experimental design, feature engineering, and model selection; communication skills enable clear reporting of results, collaboration with stakeholders, and effective documentation of experiments and production behavior.
The job is specified as Full-time, In-Office, with a standard 5 working days schedule. Compensation includes a salary range of 3.6 – 5.0 LPA and ESOPs. Perks mention Hybrid Working. These details define the employment structure and benefits available to prospective candidates.
Taken together, the responsibilities require a practitioner who can connect technical implementation with production reliability: designing and iterating models, maintaining pipelines, leveraging MLflow and Weights & Biases for reproducibility, and applying those capabilities to NLP or Computer Vision work when required. The combination of formal education, concrete library experience, systems familiarity, and communication ability makes a candidate well-suited to deliver across the described workflow.
Conclusion
In summary, this role requires end-to-end machine learning expertise: model design and implementation, robust data pipelines, iterative experimentation with MLflow and Weights & Biases, and production deployment and monitoring. Candidates should hold a relevant degree, be proficient in Python and ML libraries, understand data preprocessing and distributed systems, and bring strong analytical and communication skills. The position is full-time, in-office, five days a week, with ESOPs, hybrid working perks, and a salary range of 3.6–5.0 LPA.