GenAI Engineer

Factspan Analytics; United States; Remote; USD 150,000 - 230,000; Full time

Job summary

Factspan Analytics seeks an autonomous Generative AI Engineer to design, build, and deploy production‑grade GenAI apps on GCP. You will own end‑to‑end lifecycle from fine‑tuning LLMs to deploying containerized services with Vertex AI, GKE, and private VPC security.

You will work across product, data engineering, and business stakeholders to deliver high‑performance, bias‑mitigated AI systems for retail clients. Strong IC capabilities and hands‑on independence are essential.

Qualifications

• Education: Bachelors degree in Engineering (B.Tech / B.E. required; Masters is a plus).
• 5+ years in ML/AI with 3+ years deploying GenAI/LLM-powered apps.
• Hands-on IC capable of building AI microservices and deploying to cloud without hand-holding.
• Proficiency with GCP, Vertex AI, GKE, BigQuery, IAM/VPC security.

Responsibilities

• End‑to‑End LLM development: fine-tune and optimize LLMs for retail use cases.
• Design and implement RAG pipelines using vector stores and embeddings.
• Deploy secure, low‑latency AI apps on GCP within private VPCs.
• Build and manage CI/CD and MLOps pipelines for versioning, testing, and retraining.
• Collaborate with product and data teams to deliver high‑quality AI systems.

Skills

Python

ML/AI

LLM/GenAI

SQL

GCP

Docker

Kubernetes

CI/CD

Education

Bachelor's in Engineering

Tools

Terraform

Vertex AI

GKE

BigQuery

Job description

Position: Generative AI Engineer

Location: Bengaluru / Remote

Employment Type: Full-time

Experience: 5+ Years

About Factspan

Factspan is a global pure-play data and analytics consultancy partnering with Fortune 500 leaders across Retail, Financial Services, and Hospitality. With 350+ experts across the USA, UK, Denmark, and India, we build scalable AI/analytics solutions that convert complex data into strategic business value.

Role Overview

We are seeking an autonomous, hands‑on Generative AI Engineer (IC) to design, build, and deploy production‑grade GenAI applications and services on Google Cloud Platform (GCP). In this role, you will own the end‑to‑end lifecyclefrom fine‑tuning custom LLMs and implementing RAG pipelines to containerized deployment on Vertex AIdelivering robust, secure retail AI solutions with minimal supervision.

Key Responsibilities

• End‑to‑End LLM Development: Independently build, fine‑tune, and optimize LLMs (Transformers, T5, BERT, GPT variants) for retail use cases (conversational agents, recommendation engines, content generation).
• RAG & Semantic Search Architecture: Design and implement production Retrieval‑Augmented Generation (RAG) pipelines using vector databases (Pinecone, FAISS) and custom embedding models.
• Autonomous GCP Deployment: Deploy secure, low‑latency AI applications and microservices on GCP (Vertex AI, GKE/Kubernetes, Cloud Run/Functions) within private VPC networks adhering to strict data privacy and encryption standards.
• MLOps & Pipeline Automation: Build and manage automated CI/CD and MLOps pipelines (Docker, TFX, Terraform) for model versioning, testing, real‑time drift monitoring, and automated retraining.
• Collaboration & Quality: Partner directly with product, data engineering, and business stakeholders to translate domain requirements into high‑performance, bias‑mitigated, and compliant AI systems.

Key Requirements

• Education: Bachelors degree in Engineering (B.Tech / B.E. required; Masters is a plus).
• Core Experience: 5+ years in ML/AI, with 3+ years dedicated to developing and deploying Generative AI models and LLM‑powered applications.
• Hands‑On Independence: Proven track record as an Individual Contributor (IC) capable of building AI microservices and deploying to cloud infrastructure without hand‑holding.
• GCP & Cloud Mastery: Advanced hands‑on proficiency with Google Cloud Platform, specifically Vertex AI, GKE, BigQuery, and IAM/VPC security.

Tech Stack:

• Languages: Python (expert), SQL
• Frameworks: PyTorch, TensorFlow, Hugging Face Transformers.
• Vector Search & RAG: Pinecone, FAISS, LangChain / LlamaIndex, or native vector stores.
• MLOps & DevOps: Docker, Kubernetes, CI/CD pipelines, TFX/Kubeflow.

Preferred

Domain experience in retail or e-commerce personalization/NLP.

Active open‑source contributions, technical publications, or relevant AI community projects.