Machine Learning Engineer job opportunity at Weekday AI.

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Date2026-02-12T15:03:25.241Z bot
Weekday AI Machine Learning 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 3000000 - Rs 4000000 (ie INR 30-40 LPA) Min Experience: 5 years Location: Bangalore JobType: full-time We are looking for a skilled Machine Learning Engineer to design and deploy AI-driven conversational and personalization systems that combine deep domain knowledge with modern large language model (LLM) technologies. This role focuses on building scalable, production-grade ML pipelines, optimizing LLM orchestration, and developing intelligent recommendation and chat systems. The ideal candidate is passionate about applied AI, multilingual NLP, retrieval-augmented generation (RAG), and building high-impact ML systems that operate reliably at scale. Key Responsibilities Conversational AI & LLM Systems Design and implement AI-powered chat pipelines supporting multi-turn conversations, contextual memory, and personalization. Build and optimize LLM orchestration layers including prompt engineering, routing, fallback strategies, and model selection across platforms such as OpenAI, Claude, Qwen, LLaMA, and similar models. Develop robust evaluation frameworks to assess response quality, contextual relevance, tone alignment, and hallucination mitigation. Optimize production chat systems for scalability, latency, and cost efficiency through prompt caching, batching, and precomputation strategies. Retrieval & Knowledge Systems Implement vector search solutions using tools such as Qdrant, Pinecone, FAISS, or similar vector databases. Design retrieval-augmented generation (RAG) pipelines for contextual knowledge integration. Work with both structured datasets and unstructured textual sources to enhance contextual accuracy. ML Pipeline Development & Personalization Design and build ML pipelines for user segmentation, classification, and personalization. Develop ranking, recommendation, and content transformation systems using LLMs and traditional ML techniques. Create systems for intelligent content generation and user-specific recommendations. Multilingual NLP & Model Optimization Fine-tune and deploy models for Hindi and multilingual NLU/NLG using transformers and LLM frameworks. Experiment with and deploy open-source LLMs (e.g., Qwen, Mixtral, LLaMA) in scalable, low-latency environments. Evaluate and fine-tune closed-source LLMs to optimize performance and cost. Implement and optimize pipelines using tools such as LangChain, RAG frameworks, FAISS/Chroma vector stores, and translation APIs (e.g., IndicTrans2, NLLB). Collaboration & Cross-Functional Work Partner with product teams and domain experts to build recommendation engines and personalized insight systems. Translate business requirements into scalable ML architectures and production-ready systems. Continuously experiment, benchmark, and improve model performance. Required Skills & Qualifications Strong experience in Machine Learning and Generative AI systems. Hands-on expertise in LLM orchestration, RAG architectures, and vector databases. Experience building and deploying production-scale ML systems. Proficiency in Python and ML/NLP frameworks (Transformers, PyTorch, TensorFlow, etc.). Experience working with multilingual NLP systems. Strong understanding of model evaluation, prompt engineering, and hallucination mitigation strategies. Familiarity with scalable system design, latency optimization, and cost-efficient model deployment. Key Skills Machine Learning Engineering · Generative AI · Large Language Models (LLMs) · RAG · Vector Databases · Multilingual NLP · Model Fine-Tuning · Scalable ML Systems · Big Data

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