Machine Learning Engineer job opportunity at Weekday AI.

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Date2026-02-13T14:07:06.331Z 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 2500000 - Rs 3500000 (ie INR 25-35 LPA) Min Experience: 5 years Location: Bangalore JobType: full-time We are seeking an experienced Machine Learning Engineer to build scalable, production-grade AI systems that power intelligent conversational and personalization experiences. This role focuses on large language models (LLMs), retrieval-augmented generation (RAG), multilingual NLP, and end-to-end ML pipeline development. The ideal candidate combines strong ML fundamentals with hands-on experience in deploying LLM-powered applications in real-world, high-traffic environments. Key Responsibilities Conversational AI & LLM Systems Design and implement AI-driven chat pipelines supporting multi-turn conversations, contextual memory, and personalization. Develop and optimize LLM orchestration layers including prompt engineering, routing, and fallback strategies across multiple models. Build evaluation frameworks to assess response quality, contextual relevance, tone alignment, and hallucination control. Optimize chat systems for scalability, low latency, and cost efficiency through caching, batching, and architectural improvements. Retrieval & Knowledge Systems Implement vector search and embedding-based retrieval using vector databases such as FAISS, Pinecone, or Qdrant. Design and optimize RAG pipelines for contextual knowledge integration. Work with both structured datasets and unstructured text sources to improve contextual intelligence. ML Pipeline Development & Personalization Develop ML pipelines for classification, user segmentation, and personalization use cases. Build ranking, recommendation, and content generation systems powered by LLMs. Fine-tune and adapt models for multilingual NLU/NLG, including Hindi and other regional languages. Model Optimization & Deployment Experiment with and deploy open-source LLMs in low-latency production environments. Evaluate and fine-tune proprietary LLMs to balance performance and cost. Implement workflow orchestration using tools such as Airflow, Prefect, or Celery. Apply MLOps best practices including model versioning, evaluation, monitoring, and deployment. Ensure scalable system design leveraging caching strategies and cloud infrastructure (AWS preferred). Required Qualifications 5+ years of experience as a Machine Learning 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 dialog management and context handling. Familiarity with vector databases and modern data stack components (Postgres, Redis, S3, Kafka or similar). Strong understanding of system design, scalability, and cloud-native architectures. Experience with Docker and workflow orchestration tools. Bonus Qualifications Experience building multilingual chatbots, recommendation engines, or personal assistant systems. Exposure to advanced fine-tuning techniques such as RLHF, LoRA, PEFT, or prompt tuning. Contributions to open-source NLP/LLM projects. Domain exposure to culturally rooted or content-driven AI applications. Why Join Work on impactful AI systems combining cultural depth with cutting-edge ML technologies. Solve complex multilingual and personalization challenges using frontier tools like LLMs and RAG. Collaborate with a cross-functional team of domain experts, engineers, and product leaders. Competitive compensation with long-term growth opportunities and ownership in shaping foundational ML 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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