ML Engineer

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.

Requirements:
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