ML Engineer job opportunity at Weekday AI.

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Date2026-02-13T14:25:17.314Z bot
Weekday AI ML Engineer
Experience: 5-years
Pattern: Full-time
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loacation Bengaluru, India
loacation Bengaluru....India

This role is for one of the Weekday's clients Salary range: Rs 2500000 - Rs 3500000 (ie INR 25-35 LPA) Min Experience: 5 years Location: Bangalore JobType: full-time We are looking for an experienced ML Engineer to design and deploy scalable, production-ready AI systems powering conversational and personalization-driven applications. This role emphasizes Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), multilingual NLP, and end-to-end machine learning pipeline development. The ideal candidate has strong machine learning fundamentals and proven experience building and scaling LLM-based applications in high-traffic, real-world environments. Key Responsibilities Conversational AI & LLM Systems Architect and implement AI-powered chat systems supporting multi-turn dialogue, contextual memory, and personalization. Build and optimize LLM orchestration layers including prompt engineering, intelligent routing, and model fallback strategies. Develop evaluation frameworks to measure response quality, contextual accuracy, tone alignment, and hallucination mitigation. Optimize system performance for scalability, low latency, and cost efficiency using caching, batching, and architectural enhancements. Retrieval & Knowledge Systems Implement embedding-based vector search using databases such as FAISS, Pinecone, or Qdrant. Design and maintain RAG pipelines to integrate contextual knowledge into model outputs. Work with structured and unstructured datasets to enhance contextual intelligence. ML Pipeline Development & Personalization Build ML pipelines for classification, segmentation, and personalization use cases. Develop ranking, recommendation, and AI-driven content generation systems. Fine-tune and adapt models for multilingual NLU/NLG, including Hindi and other regional languages. Model Optimization & Deployment Deploy and optimize open-source LLMs in scalable, low-latency production environments. Evaluate and fine-tune proprietary LLMs to optimize performance and cost trade-offs. Implement workflow orchestration using tools such as Airflow, Prefect, or Celery. Apply MLOps best practices including versioning, evaluation, monitoring, and deployment automation. Design scalable cloud-native systems (AWS preferred) with effective caching strategies. Required Qualifications 5+ years of experience as an ML Engineer, Applied Scientist, or NLP Engineer. Strong proficiency in Python and ML/NLP frameworks such as PyTorch, scikit-learn, HuggingFace, and LangChain. Hands-on experience with LLMs, embeddings, vector search, prompt engineering, and model fine-tuning. Experience building conversational AI systems with dialogue management and contextual memory. Familiarity with vector databases and modern data infrastructure (Postgres, Redis, S3, Kafka, or similar). Strong understanding of system design, scalability, and cloud-native architectures. Experience with Docker and workflow orchestration tools. Preferred Qualifications Experience developing multilingual chatbots, recommendation systems, or AI assistants. Knowledge of advanced fine-tuning techniques such as RLHF, LoRA, PEFT, or prompt tuning. Contributions to open-source NLP or LLM ecosystems. Exposure to culturally driven or content-focused AI applications. Why Join Work on impactful AI systems combining deep domain relevance with cutting-edge ML innovation. Tackle complex multilingual and personalization challenges using advanced LLM and RAG frameworks. Collaborate with a cross-functional team of engineers, product leaders, and domain experts. Competitive compensation and long-term opportunities to shape foundational AI systems. Key Skills Machine Learning · Generative AI · Large Language Models (LLMs) · RAG · Conversational AI · Multilingual NLP · Vector Databases · MLOps · Scalable ML Systems

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