Job Title:
Generative AI Engineer
Company: Insight Global
Location: New delhi, Delhi
Created: 2026-01-14
Job Type: Full Time
Job Description:
Applied GenAI / ML Engineer – Immediate Joiner (ASAP Start)Company: Insight Global (on behalf of our client)Location: Remote (Full‑time with Insight Global)Compensation: 10–14 LPAAvailability: Immediate joiners strongly preferredAbout the RoleWe are looking for an Applied GenAI / ML Engineer who is passionate about leveraging LLMs, data workflows, and modern AI techniques to build intelligent, production‑ready systems. You will work hands‑on with Python, GenAI frameworks, and machine learning fundamentals to run experiments, improve data pipelines, and build automation workflows. This role is ideal for someone who thrives in fast iteration cycles, experimentation, and end‑to‑end ownership.Required Skills• Experience working with GenAI concepts, LLMs, and related frameworks• Strong Python skills with the ability to write, adapt, and optimize scripts for AI workflows• Solid understanding of classic ML fundamentals to avoid model and data pitfalls• Strong experience with Pandas and managing large, complex datasets• Cloud experience; familiarity with Azure is a plus• Ability to design and run experiments, interpret outcomes, and iterate quicklyNice‑to‑Have Skills• Experience with agentic AI frameworks• Broader hands-on exposure to ML beyond LLM use cases• Ability to take ownership of projects and expand scope from small experiments to full workflowsWhat You Will Do• Build and execute Python scripts that integrate GenAI and LLM capabilities• Apply agentic AI techniques to automate, optimize, and self‑improve workflows• Transform raw datasets into structured, functional repositories for downstream use• Run experiments, evaluate results, and propose alternative or improved approaches• Scale individual tasks into fully owned, end‑to‑end project deliverables• Break down existing processes and identify opportunities for efficiency and automation• Collaborate with product, engineering, and data teams, communicating findings clearly