AI/ML Engineer at Weekday AI

Bengaluru, karnataka, India -

Full Time

Start Date

Immediate

Expiry Date

19 Apr, 26

Salary

0.0

Posted On

19 Jan, 26

Experience

5 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

AI / Machine Learning, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), Python, PyTorch, TensorFlow, Vector Databases, MLOps / LLMOps, Cloud Platforms (AWS / Azure / GCP)

Industry

technology;Information and Internet

Description

This role is for one of the Weekday's clients Min Experience: 5 years Location: Bengaluru, Pune JobType: full-time We are looking for a skilled AI/ML Engineer to design, build, and deploy production-grade AI solutions with a strong focus on Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. In this role, you will work closely with software and machine learning engineers to deliver scalable, secure, and high-impact AI applications used in real-world business environments. This position is ideal for someone who combines strong software engineering fundamentals with hands-on experience in modern AI/ML systems and enjoys working at the intersection of research and production. Key ResponsibilitiesAI & ML System Development Design and develop scalable AI/ML solutions with emphasis on LLMs, RAG pipelines, and deep learning architectures. Own the end-to-end AI lifecycle including data ingestion, preprocessing, embedding generation, retrieval, model training, fine-tuning, evaluation, and production deployment. Build and optimize RAG systems using vector databases and retrieval frameworks to deliver accurate, grounded, and explainable AI outputs. Model Optimization & Evaluation Apply fine-tuning techniques such as LoRA and Q-LoRA to adapt large models for domain-specific use cases. Implement and experiment with reasoning models and hybrid RAG + reasoning workflows for complex enterprise scenarios. Evaluate and improve AI and RAG systems using metrics such as retrieval accuracy, relevance, faithfulness, latency, and hallucination reduction. Optimize inference performance through embedding tuning, reranking strategies, compute optimization, and efficient resource utilization. MLOps, Deployment & Integration Build and maintain MLOps / LLMOps pipelines for model training, deployment, monitoring, drift detection, and continuous improvement. Deploy AI solutions across cloud platforms (AWS, Azure, GCP), with exposure to edge deployments where applicable. Develop APIs and microservices to integrate AI and RAG capabilities into enterprise applications. Ensure compliance with data security, privacy, regulatory standards, and responsible AI practices. Collaboration & Leadership Collaborate with product, engineering, and business stakeholders to translate requirements into AI-driven solutions. Mentor junior engineers and promote best practices in AI system design and software engineering. Stay up to date with emerging research and advancements in LLMs, RAG, and applied AI. Required Qualifications Bachelor’s, Master’s, or PhD in Computer Science, Data Science, Engineering, or a related field. 5–8 years of hands-on experience in AI/ML engineering or related roles. Strong proficiency in Python. Solid understanding of machine learning fundamentals and deep learning concepts. Hands-on experience with ML frameworks such as PyTorch, TensorFlow, and scikit-learn. Practical experience or strong familiarity with LLMs, transformer-based architectures, and RAG concepts. Nice to Have Experience with vector databases (FAISS, Pinecone, Weaviate, Milvus). Familiarity with retrieval frameworks such as LangChain or LlamaIndex. Understanding of RAG evaluation techniques, prompt engineering, and grounding strategies. Exposure to frontend technologies such as React. Skills AI / Machine Learning Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Python PyTorch / TensorFlow Vector Databases MLOps / LLMOps Cloud Platforms (AWS / Azure / GCP)

Responsibilities

The AI/ML Engineer will design and develop scalable AI/ML solutions, focusing on LLMs and RAG systems. They will own the end-to-end AI lifecycle and collaborate with various stakeholders to deliver impactful AI applications.