AI Engineer- Quality Intelligence & Automation, Vice President at BlackRock

Gurugram, Haryana, India

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Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

FinanceIndustries

Requirements

Strong proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
Hands-on experience with NLP techniques and libraries (e.g., spaCy, Hugging Face Transformers)
Proven experience in fine-tuning LLMs for domain-specific tasks
Familiarity with software testing methodologies and QA lifecycle
Ability to work with structured and unstructured data sources
Excellent problem-solving and communication skills
Preferred Qualifications
LLM Expertise: Experience fine-tuning and deploying large language models (e.g., GPT, LLaMA) for domain-specific tasks such as test case generation, summarisation, and anomaly detection
Quality Engineering Knowledge: Familiarity with functional and regression testing principles, test case design, and QA lifecycle
Automation Frameworks: Hands-on experience with tools like Selenium, Playwright, or Cypress, and integrating AI into these frameworks
MLOps & Deployment: Exposure to MLOps practices including model versioning, monitoring, and CI/CD integration using platforms like MLflow, Kubeflow, or Azure ML
Cloud & Infrastructure: Experience working with cloud platforms (Azure) and containerization tools (Docker, Kubernetes) for scalable AI deployment
Data Engineering: Ability to work with large-scale datasets, including preprocessing, feature engineering, and data pipeline development
Security & Compliance Awareness: Understanding of data privacy, model interpretability, and compliance standards relevant to AI in enterprise environments
Collaboration & Communication: Proven ability to work cross-functionally with QA, DevOps, and product teams, and to communicate technical concepts to non-technical stakeholders
Exposure to Gherkin syntax and behavior-driven development (BDD) practices

Responsibilities

Design and implement AI-driven components that support quality engineering across the SDLC
Develop NLP pipelines to extract insights from requirements, user feedback, and test artifacts
Fine-tune and deploy LLMs to support intelligent test generation, summarisation, and anomaly detection
Collaborate with QA, DevOps and Product teams to integrate AI tooling into CI/CD pipelines and quality gates
Analyze historical test and defect data to identify patterns and optimize regression test coverage
Maintain and improve model performance through continuous learning and feedback loops

Skills

Key technologies and capabilities for this role

PythonTensorFlowPyTorchScikit-learnspaCyHugging Face TransformersLLM fine-tuningNLPMachine LearningCI/CDDevOpssoftware testingQA lifecycle

Questions & Answers

Common questions about this position

What are the required skills for this AI Engineer role?

Required skills include strong proficiency in Python and ML frameworks like TensorFlow, PyTorch, and Scikit-learn; hands-on experience with NLP techniques and libraries such as spaCy and Hugging Face Transformers; proven experience in fine-tuning LLMs; familiarity with software testing methodologies and QA lifecycle; ability to work with structured and unstructured data; and excellent problem-solving and communication skills.

What is the salary or compensation for this position?

This information is not specified in the job description.

Is this AI Engineer role remote or does it require office work?

This information is not specified in the job description.

What teams will I collaborate with in this role?

You will collaborate with QA, DevOps, and Product teams to integrate AI tooling into CI/CD pipelines and quality gates.

What makes a strong candidate for this AI Engineer position?

Strong candidates have required skills in Python, ML frameworks, NLP, LLMs, testing methodologies, data handling, and communication, plus preferred qualifications like LLM expertise for test generation, quality engineering knowledge, automation frameworks such as Selenium or Playwright, MLOps practices, cloud experience with Azure, data engineering, and cross-functional collaboration.