This role is for one of Weekday’s clients
Salary range: Rs 5000000 - Rs 9900000 (ie INR 50 - 99 LPA)

Min Experience: 12+ years
Location: Bengaluru
JobType: full-time

We are looking for an experienced and highly technical Staff ML Engineer with 12–20 years of experience to lead the design, development, and deployment of advanced machine learning systems at scale. The ideal candidate will have deep expertise in Machine Learning, strong software engineering fundamentals, and a proven track record of taking ML solutions from research and experimentation through production.

As a Staff ML Engineer, you will work on complex, high-impact ML problems, define technical approaches, establish engineering best practices, and collaborate closely with data scientists, software engineers, product teams, and business stakeholders. You will also provide technical leadership and mentorship to engineers while contributing to the long-term ML architecture and strategy.

Requirements

Key Responsibilities

• Design, develop, and productionize sophisticated machine learning models and systems for large-scale applications.
• Own the complete ML lifecycle, including problem formulation, data preparation, feature engineering, model development, evaluation, deployment, monitoring, and continuous improvement.
• Develop scalable and reliable ML pipelines capable of processing large datasets and supporting high-volume production workloads.
• Evaluate and implement appropriate machine learning algorithms, architectures, and techniques based on business and technical requirements.
• Improve model accuracy, scalability, latency, reliability, and overall production performance.
• Work closely with data scientists and engineering teams to transition experimental models into robust production systems.
• Establish engineering standards, design patterns, testing methodologies, and best practices for machine learning development.
• Conduct technical design reviews and make architecture decisions for complex ML systems.
• Investigate emerging developments in machine learning and assess their potential application to business and product problems.
• Mentor senior engineers and ML practitioners, providing technical guidance and helping raise engineering quality across the organization.
• Collaborate with product managers and stakeholders to translate ambiguous business problems into measurable ML objectives.
• Identify opportunities to automate processes and improve existing ML infrastructure, workflows, and model performance.
• Troubleshoot complex production issues involving data quality, model behavior, performance, and system reliability.

Must-Have Skills

• 12–20 years of overall experience in software engineering, machine learning, or closely related technical roles.
• Deep and hands-on expertise in Machine Learning and its practical application to real-world problems.
• Strong understanding of supervised and unsupervised learning, model evaluation, optimization, feature engineering, and statistical concepts.
• Strong programming skills in Python and experience developing production-quality ML software.
• Experience designing and deploying machine learning models in production environments.
• Strong understanding of ML system architecture, scalable data pipelines, model serving, and production monitoring.
• Experience working with large-scale datasets and distributed computing environments.
• Strong software engineering fundamentals, including data structures, algorithms, system design, testing, debugging, and code quality.
• Proven ability to independently solve complex technical problems and drive projects from conception to production.
• Excellent technical communication and cross-functional collaboration skills.
• Demonstrated experience mentoring engineers and providing technical leadership on complex ML initiatives.

Good-to-Have Skills

• Experience with deep learning frameworks such as PyTorch or TensorFlow.
• Exposure to MLOps, model monitoring, experimentation platforms, and ML infrastructure.
• Experience with cloud platforms such as AWS, GCP, or Azure.
• Familiarity with distributed ML systems, Kubernetes, Spark, or similar technologies.
• Experience with Generative AI, LLMs, NLP, computer vision, recommendation systems, or other specialized ML domains.
• Experience designing ML platforms or shared infrastructure used by multiple engineering teams.

Education

A Bachelor's or Master's degree in Computer Science, Machine Learning, Artificial Intelligence, Mathematics, Statistics, or a related technical discipline is preferred.