BlackRock
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Mumbai · Hybrid · Aug 16
Investment & Asset Management · 10000+ Employees · New York, US
BlackRock’s purpose is to help more and more people experience financial well-being. As a fiduciary to investors and a leading provider of financial technology, our clients turn to us for the solutions they need when planning for life's most important goals.
Hiring: Engineering
What employees say
+ Free Food Free Cab Good name on resume
− Odd timings Micro management Lack of democratic process when it comes to giving full time to interns Manager can be unreasonable and unethical at times
More reviews on the company profile.
Glassdoor median (Engineering): 2.0 LPA
Interview experience · Software Engineer(Internship)
There were 2 rounds. In the first round they asked coding question which should be written in Java or python only. The coding question they asked was count the vowels in the string
Very_easy difficulty · Bengaluru · 349 reports
More interview insightsJob description
Overview
Responsible for preparing the 'big data' infrastructure to be analyzed by Data Scientists and Analysts.
Responsibilities
- Define and drive the technical strategy and roadmap for applied AI capabilities within Hyperion, aligning AI investments with business priorities across Private Markets.
- Lead the architecture, design, and scaling of complex multi-agent workflows using frameworks such as LangGraph or similar orchestration frameworks.
- Drive the end-to-end lifecycle of AI applications, from proof of concept to MVP to production deployment, including data pipelines, backend services, and platform integration, leveraging Agile methodologies to deliver iteratively and at scale.
- Lead the integration of LLM-powered capabilities into core business products and platform services, ensuring high availability, low latency, resilience, and maintainability. Establish robust evaluation frameworks to assess agent behavior, trajectories, decision quality, and system performance, with a strong focus on reliability, explainability, and business relevance.
- Build, mentor, and lead a high-performing team of engineers and applied AI practitioners, fostering technical excellence, collaboration, and continuous learning.
- Drive the development of reusable platform capabilities and patterns that enable scalable adoption of AI across products and workflows, rather than one-off implementations.
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