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Tier-1 brand, popular ML/AI entry role, broad GenAI/MLOps requirements and likely metro hiring increase competition.
Core ML/AI and MLOps skills are transferable; finance domain knowledge moderately increases fit.
Multiple mandatory GenAI, cloud, backend and MLOps skills required, increasing screening rigidity.
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Build, test, and deploy production-ready generative AI applications, transforming prototypes into stable enterprise-grade cloud services.
Develop and maintain backend pipelines for GenAI workflows including RAG systems, multi-agent frameworks, and semantic search under senior guidance.
Support CI/CD and monitoring for ML models on GCP, optimize inference performance to control latency and cloud cost, and manage data and embedding pipelines linked to vector databases.
Entry level position, work experience required: Not explicitly mentioned in the JD.
Proficiency in Python and backend software engineering including APIs, object-oriented design, and microservices.
Hands-on experience with Google Cloud Platform (Cloud Run, GKE/Kubernetes, BigQuery) or equivalent cloud providers, and familiarity with Git, CI/CD, and infrastructure as code basics.
Practical exposure to foundation model APIs (e.g., OpenAI, Gemini) and cloud MLOps concepts including monitoring and logging.
Candidates with strong backend software development skills and a DevOps mindset focused on production-ready AI applications.
Comfortable working in cloud environments, especially GCP, with a basic understanding of MLOps pipelines and scalable AI workflow engineering.
Analytical mindset with a keen interest in learning commodity and financial markets, able to collaborate with math experts and senior architects.