Data Scientist

Hiringhood

Gurugram District

On-site

INR 1,500,000 - 2,300,000

Full time

8 days ago
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Job summary

Hiringhood is seeking an experienced AI/ML Engineer to design and deploy scalable AI systems in production. You will work on RAG pipelines, LLM workflows, and APIs to integrate AI solutions into existing products.

The role emphasizes reliability, performance optimization, and collaboration with engineering and business teams to define AI use cases and deliver robust solutions.

Qualifications

  • 3–4 years of experience building AI/ML applications with strong Python and software engineering skills.
  • Strong proficiency in Python.
  • Understanding of AI system design, evaluation, monitoring, and performance optimization.
  • Experience developing, deploying, and maintaining production AI applications.
  • Strong software engineering fundamentals, including clean code, modular design, debugging, and testing.
  • RAG architectures.
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Good understanding of NLP concepts.
  • Familiarity with API development and integration.
  • Ability to work independently as well as in collaborative team environments.

Responsibilities

  • Design scalable, reliable AI systems with a focus on production readiness, maintainability, and performance.
  • Perform root cause analysis and debugging of AI pipelines to improve accuracy, consistency, and reliability.
  • Develop automated evaluation, validation, and testing strategies for AI applications.
  • Design reliable LLM workflows using structured outputs, validation, retrieval, and business rules.
  • Create and maintain RAG pipelines and AI agent frameworks.
  • Perform data analysis, model evaluation, and performance optimization.
  • Develop APIs and integrate AI solutions into existing products and platforms.
  • Collaborate with cross-functional teams including engineering, and business stakeholders to define AI use cases and deliver solutions.
  • Monitor, troubleshoot, and continuously improve deployed AI systems.

Skills

Python
AI system design
RAG architectures
NLP concepts
API development

Tools

TensorFlow
PyTorch
Scikit-learn
Docker

Job description

Must have
  • 3–4 years of experience building AI/ML applications with strong Python and software engineering skills.
  • Strong proficiency in Python.
  • Understanding of AI system design, evaluation, monitoring, and performance optimization.
  • Experience developing, deploying, and maintaining production AI applications.
  • Strong software engineering fundamentals, including clean code, modular design, debugging, and testing.
  • RAG architectures
  • Experience with machine learning frameworks such as TensorFlow, PyTorch, or Scikit-learn.
  • Good understanding of NLP concepts.
  • Familiarity with API development and integration.
  • Ability to work independently as well as in collaborative team environments.
Good to have
  • Experience with vector databases such as Pinecone, Weaviate, FAISS, ChromaDB.
  • Knowledge of cloud platforms such as AWS, Azure, or GCP.
  • Experience with Docker, and deployment pipelines.
  • Exposure to fintech products, platforms, or domain-specific use cases.
Roles & Responsibilities
  • Design scalable, reliable AI systems with a focus on production readiness, maintainability, and performance.
  • Perform root cause analysis and debugging of AI pipelines to improve accuracy, consistency, and reliability.
  • Develop automated evaluation, validation, and testing strategies for AI applications.
  • Design reliable LLM workflows using structured outputs, validation, retrieval, and business rules.
  • Create and maintain RAG pipelines and AI agent frameworks.
  • Perform data analysis, model evaluation, and performance optimization.
  • Develop APIs and integrate AI solutions into existing products and platforms.
  • Collaborate with cross-functional teams including engineering, and business stakeholders to define AI use cases and deliver solutions.
  • Monitor, troubleshoot, and continuously improve deployed AI systems.
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