Role Overview

We are seeking a highly skilled Generative AI Engineer to design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs), multimodal models, and advanced machine learning techniques. You will work closely with product, data, and engineering teams to build scalable AI solutions that deliver measurable business impact.

Shift Timing: 1 to 10 pm

Work Mode: Remote 

Key Responsibilities

 Model Development & Integration

• Design and implement solutions using LLMs (e.g., GPT, Claude, LLaMA)
• Develop prompt engineering strategies and evaluation frameworks
• Fine-tune open-source models for domain-specific use cases
• Build Retrieval-Augmented Generation (RAG) pipelines

AI Application Engineering

• Develop APIs and backend services to serve AI models
• Build chatbots, copilots, and AI assistants
• Implement vector search solutions (e.g., Pinecone, FAISS, Weaviate)
• Integrate AI services into web/mobile applications

Data & Infrastructure

• Build data pipelines for model training and inference
• Optimize model performance, latency, and cost
• Deploy models on cloud platforms (AWS/Azure/GCP)
• Implement MLOps best practices (CI/CD for ML, monitoring, versioning)

Evaluation & Governance

• Design model evaluation metrics (hallucination, toxicity, bias)
• Ensure responsible AI practices and compliance
• Conduct A/B testing and continuous improvement

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, AI, Data Science, or related field
• 3+ years of experience in AI/ML engineering
• Strong proficiency in Python
• Experience with:
• PyTorch / TensorFlow
• LLM frameworks (LangChain, LlamaIndex)
• REST APIs (FastAPI, Flask)
• Vector databases
• Solid understanding of NLP and transformer architectures
• Experience with cloud platforms (AWS, Azure, or GCP)

Preferred Qualifications

• Experience fine-tuning open-source LLMs
• Hands-on experience with RAG architectures
• Knowledge of distributed systems and scalable infrastructure
• Exposure to multimodal AI (vision + text models)
• Familiarity with Kubernetes and Docker
• Understanding of AI safety and governance frameworks

Nice to Have

• Experience building AI SaaS products
• Knowledge of reinforcement learning from human feedback (RLHF)
• Contributions to open-source AI projects
• Publications in ML/AI conferences

Key Skills

• Generative AI & LLMs
• Prompt Engineering
• RAG & Vector Search
• NLP & Deep Learning
• API Development
• Cloud & MLOps
• AI Governance