BlackRock Artificial Intelligence Engineer Recruitment 2026 | Associate AI Engineer Jobs in Gurgaon & Mumbai
Introduction
Are you passionate about Artificial Intelligence, Machine Learning, Large Language Models (LLMs), Generative AI, Data Engineering, and Financial Technology? BlackRock is hiring Artificial Intelligence Engineers (Associate) for its Portfolio Management Group (PMGTech) in Gurgaon and Mumbai, India.
This exciting opportunity is designed for AI professionals who want to build cutting-edge Generative AI applications that directly support investment research and portfolio management. As part of BlackRock’s technology organization, you will collaborate with investment researchers, portfolio managers, and engineering teams to design, develop, and deploy production-grade AI solutions that improve investment decision-making.
If you enjoy solving complex business challenges using AI, cloud technologies, and modern software engineering practices, this role offers an outstanding opportunity to work with one of the world’s largest asset management firms.
Job Overview
Position: Artificial Intelligence Engineer (Associate)
Company: BlackRock
Business Unit: Portfolio Management Group (PMGTech)
Location: Gurgaon, Haryana & Mumbai, Maharashtra
Employment Type: Full-Time
Experience: 3+ Years
Qualification: Bachelor’s or Master’s Degree in Computer Science, Data Science, Artificial Intelligence, Machine Learning, or equivalent
About BlackRock
Founded in 1988 and headquartered in New York, USA, BlackRock is the world’s largest asset management company, managing trillions of dollars in assets for institutions, governments, corporations, and individual investors across the globe.
BlackRock’s mission is to help more people achieve financial well-being by providing innovative investment solutions, advanced financial technologies, and world-class risk management capabilities.
One of BlackRock’s biggest technology innovations is Aladdin, an integrated investment management and risk analytics platform used by financial institutions worldwide.
Today, BlackRock continues investing heavily in Artificial Intelligence, Cloud Computing, Data Science, and Financial Engineering to transform the future of investment management.
About PMGTech
PMGTech is the technology organization within BlackRock’s Portfolio Management Group (PMG).
The team combines:
- Investment Research
- Artificial Intelligence
- Data Engineering
- Alternative Data
- Software Engineering
- Generative AI
- Research Platforms
Its objective is to build AI-powered tools that enhance investment research, improve portfolio decisions, and create competitive advantages through technology.
The team works closely with investment professionals across multiple asset classes, including equities, fixed income, and multi-asset portfolios.
About the Artificial Intelligence Engineer Role
As an Artificial Intelligence Engineer, you will design and develop AI-powered research applications that help investment professionals analyze financial information more efficiently.
You will work directly with portfolio managers, investment researchers, and software engineers to transform research workflows using modern AI technologies such as Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), vector databases, intelligent agents, and cloud-native applications.
This role combines AI engineering, software development, product thinking, and financial technology to create scalable production systems that deliver meaningful business value.
Key Responsibilities
As an Artificial Intelligence Engineer, you will:
- Design scalable AI application architectures.
- Build Generative AI research applications.
- Develop Retrieval-Augmented Generation (RAG) pipelines.
- Implement intelligent agent workflows.
- Design vector search and embedding solutions.
- Build backend services using Python and SQL.
- Develop AI-powered research tools from proof of concept to production.
- Collaborate with investment researchers and portfolio managers.
- Integrate enterprise data sources into AI applications.
- Implement monitoring and evaluation frameworks.
- Improve AI safety, reliability, and performance.
- Evaluate new AI models and APIs.
- Build intuitive user interfaces for investor-facing applications.
- Support production deployment and application scalability.
Educational Qualification
Candidates should possess:
- Bachelor’s Degree in Computer Science
- Master’s Degree in Computer Science
- Data Science
- Artificial Intelligence
- Machine Learning
- Related Engineering Discipline
Experience Required
- Minimum 3+ years of experience developing AI, ML, or data-intensive applications.
Experience in financial services or investment research is an added advantage.
Skills Required and Why They Matter
1. Python
Python is the primary programming language for AI development.
Why It Is Important
- Builds AI applications.
- Develops ML pipelines.
- Automates workflows.
- Supports backend services.
Python remains the industry’s leading AI programming language.
2. SQL
SQL manages structured enterprise data.
Why It Is Important
- Retrieves financial data.
- Supports analytics.
- Builds data pipelines.
- Enables AI applications to access business information.
3. Generative AI
Generative AI creates intelligent systems capable of producing human-like responses.
Why It Is Important
- Automates research.
- Generates insights.
- Summarizes information.
- Improves investor productivity.
Generative AI is a core component of this role.
4. Large Language Models (LLMs)
LLMs power conversational AI systems.
Why It Is Important
- Understand natural language.
- Analyze documents.
- Answer research questions.
- Generate investment summaries.
Examples include GPT models, Llama, and other open-source foundation models.
5. Retrieval-Augmented Generation (RAG)
RAG combines LLMs with enterprise knowledge.
Why It Is Important
- Produces accurate responses.
- Reduces hallucinations.
- Retrieves trusted enterprise information.
- Enhances AI reliability.
RAG has become one of the most valuable enterprise AI skills.
6. Prompt Engineering
Prompt engineering improves AI responses.
Why It Is Important
- Controls model behavior.
- Improves answer quality.
- Optimizes business workflows.
- Enhances user experience.
7. Agentic AI
Agentic AI allows systems to plan and perform multi-step tasks.
Why It Is Important
- Automates research workflows.
- Performs reasoning.
- Coordinates multiple AI tools.
- Improves productivity.
This is an emerging area of enterprise AI.
8. Vector Databases & Embeddings
Modern AI search relies on semantic embeddings.
Why It Is Important
- Supports similarity search.
- Powers document retrieval.
- Enables enterprise knowledge systems.
- Improves contextual responses.
9. Fine-Tuning
Fine-tuning adapts AI models to business-specific tasks.
Why It Is Important
- Improves accuracy.
- Learns domain knowledge.
- Enhances model performance.
- Delivers customized AI solutions.
10. Data Engineering
AI systems depend on reliable data.
Why It Is Important
- Builds scalable pipelines.
- Cleans enterprise data.
- Integrates multiple sources.
- Supports production AI systems.
11. Backend Development
Backend services connect AI with enterprise applications.
Why It Is Important
- Supports APIs.
- Manages business logic.
- Handles data processing.
- Enables production deployment.
12. Cloud Computing
Cloud platforms power modern AI infrastructure.
Why It Is Important
- Provides scalability.
- Enables model deployment.
- Supports distributed computing.
- Improves reliability.
Experience with AWS, Azure, or Google Cloud is valuable.
13. Front-End Development
Investor-facing applications require intuitive interfaces.
Why It Is Important
- Improves usability.
- Enhances user experience.
- Supports visualization.
- Simplifies AI interaction.
14. AI Evaluation & Monitoring
Production AI requires continuous evaluation.
Why It Is Important
- Measures quality.
- Detects failures.
- Improves reliability.
- Maintains system performance.
15. Communication Skills
AI Engineers work directly with investment professionals.
Why It Is Important
- Understands business needs.
- Explains technical concepts.
- Improves collaboration.
- Delivers successful AI products.
Preferred Qualifications
Candidates with the following experience will have an advantage:
- Financial technology applications
- Investment research systems
- Portfolio management tools
- Open-source LLMs
- Cloud platforms (AWS, Azure, GCP)
- Full-stack application development
- Financial markets knowledge
- Investment management domain expertise
Why Join BlackRock?
Working at BlackRock provides numerous career advantages:
- Work on production-grade AI applications.
- Collaborate with world-class investment professionals.
- Exposure to cutting-edge Generative AI technologies.
- Opportunity to build AI products used globally.
- Hybrid work environment.
- Competitive compensation and annual performance bonus.
- Comprehensive healthcare benefits.
- Retirement and financial wellness programs.
- Tuition reimbursement.
- Flexible Time Off (FTO).
- Strong learning and career development programs.
- Global technology career pathways.
Career Growth Opportunities
Professionals joining this role can progress toward positions such as:
- Senior AI Engineer
- Lead AI Engineer
- Machine Learning Engineer
- Generative AI Architect
- AI Platform Engineer
- Principal Engineer
- Engineering Manager
- Director of Engineering
- AI Solutions Architect
- Head of AI Engineering
BlackRock offers structured technical and leadership career paths that enable employees to grow based on their interests and expertise.
Who Should Apply?
This opportunity is ideal for candidates who:
- Have 3+ years of AI or ML experience.
- Enjoy building Generative AI applications.
- Are proficient in Python and SQL.
- Have experience with LLMs and RAG architectures.
- Are interested in financial technology.
- Want to build enterprise AI systems.
- Enjoy collaborating with business stakeholders.
- Have strong analytical and communication skills.
Conclusion
The BlackRock Artificial Intelligence Engineer Recruitment 2026 offers an exceptional opportunity to work at the intersection of Artificial Intelligence, Data Engineering, and Investment Management. By joining PMGTech, you will contribute to next-generation AI platforms that enhance investment research, improve portfolio management, and shape the future of financial technology.
With hands-on exposure to Generative AI, Large Language Models, Retrieval-Augmented Generation, cloud platforms, and enterprise-scale AI systems, this role provides an outstanding platform for engineers seeking long-term growth in AI and fintech.