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Tier-1 brand, popular ML title, mid-level experience range, and metro location increase applicant competition.
Medium because core ML skills transfer broadly, but financial domain and retrieval expertise add specialization.
Multiple mandatory requirements (3+ years, Python, ML pipelines, LLM toolkits, SQL) make shortlisting highly strict.
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Develop and deploy production-grade machine learning systems focused on retrieval-driven AI agents and essential AI toolkits powered by LLMs and proprietary data.
Ensure ML solutions are reliable, scalable, transparent, and deliver measurable user value across end-to-end system design, data, retrieval, modeling, and deployment.
Collaborate cross-functionally with ML engineers, backend teams, and product managers to influence architecture and drive ML product innovation and lifecycle management.
Bachelor's degree or higher in Computer Science, Engineering, or related field.
Minimum 3 years of hands-on industry experience in machine learning, NLP, and information retrieval production systems design and maintenance.
Strong proficiency in Python and ability to write SQL queries for specific access patterns.
Experience with ML pipelines (data processing, training, inference, maintenance, evaluation, versioning, experimentation) and practical understanding of backend deployment and production operations.
Mid-to-senior level ML engineer experienced in building robust, scalable, and high-accuracy ML systems focused on production deployment of LLM-powered AI agents and retrieval-driven solutions.
Technically adept in full ML lifecycle management and system design with strategic impact on key architectural decisions and technical debt reduction.
Operates effectively in dynamic, fast-paced environments, partnering intensively with multidisciplinary teams including SMEs to align ML features with business needs and product vision.