Generative AI Engineer

Job Title:

Generative AI Engineer

Location:

Gurgaon (Hybrid)

Experience:

3+ Years

Notice Period:
Immediate Joiners Preferred

Role Overview

We are looking for a highly skilled Generative AI Engineer to design and deploy advanced AI-driven search, recommendation, and LLM-powered systems. The ideal candidate will have strong hands-on experience with Transformer architectures, LLM fine-tuning, semantic search, and production-grade ML systems.

This role requires deep technical expertise in machine learning, natural language processing, and scalable deployment in enterprise environments.

Key Responsibilities

Search & Recommendation Development

Lead end-to-end design, development, and deployment of search, personalization, and recommendation systems.
Build scalable solutions that significantly improve user experience and business KPIs.
Optimize ranking models and dense retrieval systems.
Transformer-Based Model Implementation

Fine-tune and optimize models such as BERT, RoBERTa, and encoder architectures for:
Semantic Search
Relevance Ranking
Query Understanding
Embedding Generation
Implement dense vector search and retrieval systems.
Large Language Model (LLM) Innovation

Research and prototype LLM-based solutions.
Work on model selection, prompt engineering, LoRA-based fine-tuning, and quantization.
Design and implement RAG (Retrieval-Augmented Generation) systems using vector databases.
Build advanced retrieval pipelines with frameworks such as Hugging Face, Lang Chain, and Llama Index.
⚙ ML Productionization (MLOps)

Build, train, validate, and deploy ML models into scalable, low-latency production systems.
Ensure reliability, observability, and maintainability of ML services.
Collaborate with engineering teams to integrate models into real-world applications.
Data Strategy & Feature Engineering

Work closely with Data Engineering to define datasets and feature pipelines.
Ensure data quality, consistency, and governance across ML workflows.
Develop innovative features for ranking and recommendation models.
Evaluation & Optimization

Define and track KPIs such as NDCG, CTR, latency, perplexity, recall, and precision.
Continuously improve model accuracy, robustness, and performance.
Conduct A/B testing and experimentation.
Essential Technical Qualifications

MS/PhD in Computer Science, Data Science, Engineering, or equivalent experience.
3+ years of hands-on experience in ML/AI engineering.
Expert-level proficiency in Python.
Strong experience with ML/DL libraries (Num Py, Pandas, Scikit-learn).
Deep experience with PyTorch or Tensor Flow.
Proven hands-on work with Transformer models (BERT, encoder-only models) for IR/NLU.
Practical experience with LLM fine-tuning and deployment.

Experience with frameworks such as Hugging Face, Lang Chain, Llama Index.
Strong understanding of classical ML algorithms and statistical modeling.
Direct experience building search ranking systems, recommendation engines, or vector-based search.

Experience with cloud platforms (AWS, GCP, or Azure).

Experience with MLOps tools such as MLFlow, Kubeflow, Docker, Kubernetes.

Preferred Qualifications

Experience with LoRA, PEFT, or model quantization.
Hands-on work with vector databases (Pinecone, Weaviate, FAISS, Milvus).
Exposure to large-scale data systems and distributed training.
Why Join?

Work on cutting-edge Generative AI systems.
High-impact role influencing search & recommendation architecture.
Collaborative and innovation-driven environment.