AI ML Engineer

Role & responsibilities

• Define and execute the

  AI/ML roadmap

  for personalized recommendation features.
• Develop and

  deploy ML models

  into production using tools like

  FastAPI, Docker, MLFlow

  , and

  Airflow

  .
• Build and optimize

  recommendation systems

  using

  collaborative filtering

  ,

  content-based filtering

  , and

  knowledge-based techniques

  .
• Integrate

  agentic AI workflows

  using LLMs (OpenAI APIs, Vertex AI) and Retrieval-Augmented Generation (RAG).
• Work with

  structured and unstructured data

  including user behavior, travel logs, and merchant/product metadata.
• Use

  Perplexity-style LLM architectures

  to enhance explainability and product/merchant discovery.
• Collaborate across backend, frontend, and data engineering teams for

  end-to-end system integration

  .
• Ensure scalable and secure deployment on

  cloud platforms

  (AWS, GCP, or Azure).

Preferred candidate profile

• 2 to 5 years of IT industry experience

  &

  min 2 years

  ML/AI model development, roadmap ownership, and production deployment.
• Proven experience deploying ML models with

  FastAPI, Docker, MLFlow

  , and

  Airflow

  .
• Strong hands-on expertise in

  Python

  ,

  Pandas

  ,

  scikit-learn

  , and

  recommendation libraries

  like

  LightFM

  or

  XGBoost

  .
• Solid understanding of

  recommender systems

  , including hybrid approaches combining collaborative and content-based filtering.
• Familiarity with

  agentic AI systems

  and

  LLM workflows

  (RAG, prompt engineering, conversational AI).
• Working knowledge of

  Vertex AI

  ,

  OpenAI APIs

  , or

  Perplexity-style

  architectures for LLM-enhanced recommendations.
• Exposure to

  cloud environments

  (AWS/GCP/Azure) for training and serving ML models.

• Immediate availability

  is a must.
• Strong problem-solving skills, adaptability, and the ability to work independently in a fast-paced environment.