Point your AI agent at freehire and let it find you a job.

Get the CLI →

Sampoorna Consultants

NewBe an early applicant

Senior AI Engineer - G3 - ST

Posted Updated
Discussion

Summary

Senior AI Engineer (80% ML engineering) building and scaling production ML systems for ecommerce search, retrieval, matching, ranking, and personalization, including GenAI/agentic experiences. Core stack: Python, Databricks pipelines, and Kubernetes/AKS deployment with experimentation and MLOps/LLMOps.

Senior AI Engineer - G3 - ST - J50304

Role Overview

We are hiring a Senior AI Engineer / Applied Scientist to build and scale ML systems powering ecommerce search, retrieval, and personalization. This is an 80% ML Engineering role focused on production systems, rapid experimentation, and measurable business impact.You will own end-to-end AI solutions across matching, retrieval, ranking, and GenAI-powered experiences, from prototyping to deployment at scale.

Required Qualifications

  • 5-8+ years of experience in ML Engineering / Applied AI
  • Strong experience with:
    • Search, retrieval, and ranking systems
    • NLP / embeddings / deep learning models
    • Experimentation and evaluation methodologies
  • Proven track record of building production-grade ML systems
  • Strong proficiency in Python
  • Hands-on experience with Databricks and Kubernetes-based deployment (AKS preferred)
  • Ability to learn quickly and operate independently in a fast-evolving AI landscape

Core Responsibilities

  • Design, build, and improve ML models for:
    • Product matching / entity resolution
    • Semantic retrieval and search relevance
    • Ranking and recommendation systems
  • Develop and optimize multi-stage ranking pipelines (candidate generation ranking re-ranking)
  • Run experimentation frameworks (offline evaluation, A/B testing) to drive continuous improvement
  • Apply reinforcement learning / bandits for personalization and ranking optimization
  • Build and deploy GenAI and agentic AI systems for search, discovery, and content use cases
  • Productionize ML systems using Databricks-based pipelines and deploy services via AKS (Azure Kubernetes Service)
  • Design scalable, reliable ML infrastructure with focus on latency, throughput, and cost efficiency
  • Monitor model performance and continuously iterate using MLOps/LLMOps best practices
  • Independently scope ambiguous problems and drive them from idea production

Skills

What Senior AI Engineering jobs ask for — and how much of it you have →
Apply

See also

AI Engineering jobs by country — openings, pay and top skills →

Tailor your CV for this role?

We couldn't check your fit for this role — add a CV to your profile to see it next time.