Jobs

Artificial Intelligence Engineer, Portfolio Management Group, Associate
Mumbai, India
·
On-site
·
Full-time
·
1w ago
Benefits & Perks
•401(k)
•Healthcare
•Tuition Reimbursement
•Parental Leave
•Flexible Hours
•401k
•Healthcare
•Parental Leave
•Flexible Hours
Required Skills
Python
SQL
Machine Learning
Deep Learning
PyTorch
TensorFlow
Scikit-learn
Generative AI
NLP
About this role
About Global Fixed Income Team
Black Rock’s active fixed income portfolio strategies stretch across a range of investment styles and segments of the fixed income universe. The breadth and depth of our platform puts us in a strong position to potentially deliver attractive returns in various market environments. With a focus on alpha generation and risk management, the team uses a top-down view from lead portfolio managers and bottom-up expertise from our sector specialists.
Responsibilities:
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Design and implement scalable generative AI and machine learning solutions for business problems.
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Develop, train, and fine-tune ML and deep learning models using frameworks such as Py Torch, Keras, Tensor Flow or Scikit-learn.
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Collaborate with data engineers and software developers to integrate AI models into production systems through APIs, vector databases, and orchestration frameworks (e.g., Lang Chain, Llama Index).
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Experiment with and evaluate generative AI techniques such as retrieval-augmented generation (RAG), reinforcement learning from human feedback (RLHF), and model distillation.
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Analyze large datasets to extract meaningful insights and features for model training.
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Evaluate model performance using statistical methods, experiment tracking, A/B testing and LLM evaluation techniques.
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Ensure compliance with responsible AI standards — including fairness, transparency, and data governance.
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Stay updated with advancements in AI & ML to propose innovative solutions.
Education and Qualifications/Skills:
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Bachelor's or Master's degree in Computer Science/Engineering or related field.
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3-7 years of experience.
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Good understanding of the foundations and working principles of machine learning algorithms, linear algebra, probability theory, statistics, and optimization theory.
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Knowledge of at least one of the following domains: generative AI, statistical learning, optimization, NLP, deep learning or time series analysis.
Should have strong proficiency in the following:
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Languages**:** Python, SQL
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Frameworks**:** Py Torch, Tensor Flow, Scikit-learn, Hugging Face, Lang Chain, Llama Index
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Data & Storage: Spark, Pandas, Snowflake, FAISS, Pinecone, Chroma
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Familiarity with cloud platforms (GCP, Azure, AWS)
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Proficiency in version control systems (e.g., Git/GitHub).
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Strong analytical thinking skills with an ability to creatively solve business problems, innovating new techniques as needed.
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Excellent verbal and written communication skills.
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Self-driven and results oriented team player with ability to collaborate effectively across geographies/time zones
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Passion for solving interesting and relevant real-world problems in Finance using a data science and AI approaches.
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Prior experience in financial services or large-scale enterprise systems is a plus.
Our benefits
To help you stay energized, engaged and inspired, we offer a wide range of benefits including a strong retirement plan, tuition reimbursement, comprehensive healthcare, support for working parents and Flexible Time Off (FTO) so you can relax, recharge and be there for the people you care about.
Our hybrid work model
Black Rock’s hybrid work model is designed to enable a culture of collaboration and apprenticeship that enriches the experience of our employees, while supporting flexibility for all. Employees are currently required to work at least 4 days in the office per week, with the flexibility to work from home 1 day a week. Some business groups may require more time in the office due to their roles and responsibilities. We remain focused on increasing the impactful moments that arise when we work together in person – aligned with our commitment to performance and innovation. As a new joiner, you can count on this hybrid model to accelerate your learning and onboarding experience here at Black Rock.
About Black Rock
At Black Rock, we are all connected by one mission: to help more and more people experience financial well-being. Our clients, and the people they serve, are saving for retirement, paying for their children’s educations, buying homes and starting businesses. Their investments also help to strengthen the global economy: support businesses small and large; finance infrastructure projects that connect and power cities; and facilitate innovations that drive progress.
This mission would not be possible without our smartest investment – the one we make in our employees. It’s why we’re dedicated to creating an environment where our colleagues feel welcomed, valued and supported with networks, benefits and development opportunities to help them thrive.
For additional information on Black Rock, please visit @blackrock | Twitter: @blackrock | LinkedIn: www.linkedin.com/company/blackrock
Black Rock is proud to be an Equal Opportunity Employer. We evaluate qualified applicants without regard to age, disability, family status, gender identity, race, religion, sex, sexual orientation and other protected attributes at law.
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About BlackRock

BlackRock
PublicMultinational investment company.
10,001+
Employees
New York City
Headquarters
Reviews
3.5
5 reviews
Work Life Balance
2.2
Compensation
3.5
Culture
3.0
Career
4.2
Management
2.8
Pros
Fast-paced learning and development opportunities
Advanced technology and global platform
Good benefits and retirement packages
Cons
Poor work-life balance
High stress and competitive environment
Toxic work culture in some areas
Salary Ranges
7,021 data points
Junior/L3
L2
L3
L4
L5
L6
Junior/L3 · Analyst
1,924 reports
$118,963
total / year
Base
$100,050
Stock
-
Bonus
$18,913
$81,954
$175,627
Interview Experience
6 interviews
Difficulty
3.3
/ 5
Duration
14-28 weeks
Offer Rate
17%
Interview Process
1
HireVue
2
Online Assessment
3
Final Round/Superday
Common Questions
Technical interviews
Behavioral questions
Role-specific assessments
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