AI engineer at Weekday AI

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

27 May, 26

Salary

5000000.0

Posted On

26 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Artificial Intelligence, Large Language Models, ChatBot Development, Natural Language Processing, Prompt Engineering, Retrieval-Augmented Generation, Model Fine-Tuning, Vector Stores, Python, PyTorch, TensorFlow, Hugging Face Transformers, LangChain, REST APIs, MLOps, Cloud Platforms

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Salary range: Rs 4000000 - Rs 5000000 (ie INR 40-50 LPA) Min Experience: 4 years Location: Bengaluru JobType: full-time We are seeking a highly skilled and innovative AI Engineer with 4–6 years of experience in Artificial Intelligence, specializing in Large Language Models (LLMs) and ChatBot development. The ideal candidate will have strong hands-on experience designing, developing, and deploying AI-driven conversational systems that deliver intelligent, context-aware, and scalable solutions. You will work at the intersection of machine learning, natural language processing (NLP), and product engineering to build next-generation AI applications. Key Responsibilities Design, develop, and deploy AI-powered applications leveraging Large Language Models (LLMs). Build intelligent ChatBots and conversational agents for web, mobile, and enterprise platforms. Fine-tune and optimize foundation models for domain-specific use cases. Develop prompt engineering strategies to enhance response accuracy, safety, and contextual understanding. Integrate LLMs with APIs, databases, vector stores, and enterprise systems. Implement Retrieval-Augmented Generation (RAG) pipelines for knowledge-grounded responses. Ensure conversational AI systems are scalable, secure, and production-ready. Monitor, evaluate, and continuously improve model performance using appropriate metrics. Collaborate with product managers, designers, and backend/frontend engineers to deliver end-to-end AI solutions. Stay up to date with advancements in Artificial Intelligence and emerging LLM technologies. Required Skills & Qualifications 4–6 years of professional experience in Artificial Intelligence and machine learning. Strong understanding of Large Language Models (LLMs), transformer architectures, and NLP concepts. Hands-on experience working with OpenAI, open-source LLMs (such as LLaMA, Mistral, or similar), or enterprise-grade language models. Experience building and deploying ChatBots using conversational frameworks. Proficiency in Python and AI/ML libraries such as PyTorch, TensorFlow, Hugging Face Transformers, or LangChain. Experience with prompt engineering, model fine-tuning, embeddings, and vector databases. Knowledge of REST APIs, microservices architecture, and cloud platforms (AWS, Azure, or GCP). Understanding of model evaluation, bias mitigation, and responsible AI principles. Familiarity with CI/CD pipelines and MLOps practices. Preferred Qualifications Experience implementing Retrieval-Augmented Generation (RAG) systems. Exposure to reinforcement learning or human-in-the-loop training approaches. Knowledge of speech-to-text and text-to-speech integrations for voice-enabled ChatBots. Experience working in Agile development environments. Strong problem-solving skills and ability to translate business requirements into AI-driven solutions. What You’ll Bring Passion for Artificial Intelligence and innovation in conversational technologies. Ability to design scalable AI architectures from prototype to production. Strong communication skills to explain complex AI concepts to technical and non-technical stakeholders. Ownership mindset with a focus on quality, performance, and user experience.

Responsibilities

The role involves designing, developing, and deploying AI-powered applications, primarily focusing on building intelligent ChatBots and conversational agents leveraging Large Language Models. Key tasks include fine-tuning foundation models, developing prompt engineering strategies, and integrating LLMs with various enterprise systems.