Member of Technical Staff - ML job opportunity at Weekday AI.

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Date2026-01-14T07:20:32.899Z bot
Weekday AI Member of Technical Staff - ML
Experience: 2-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 4000000 (ie INR 25-40 LPA) Min Experience: 2 years Location: Bengaluru JobType: full-time We are looking for a driven Member of Technical Staff – Machine Learning with 2–3 years of hands-on experience in Python development, API engineering, and working with Large Language Models (LLMs). This role focuses on building and scaling ML-powered features, integrating AI capabilities into production systems, and contributing to the development of AI-first products. Key Responsibilities Design, build, and maintain Python-based machine learning pipelines and services . Develop, implement, and maintain RESTful APIs and integrations with internal and external systems. Integrate and operationalize LLM models (e.g., OpenAI, Anthropic, Gemini) within production applications. Create and optimize prompts, prompt templates, and prompt chains for various LLM-driven use cases. Collaborate closely with product and engineering teams to translate business requirements into ML solutions. Monitor model performance and optimize inference, latency, and reliability. Follow best practices for code quality, testing, scalability, and maintainability . Contribute to system design and architecture discussions for AI-driven platforms and products. Required Skills & Experience Strong proficiency in Python , with experience using FastAPI or Flask . Solid experience in API development and integration , including REST standards, authentication, versioning, and documentation. Hands-on experience with LLM integration , embeddings, and vector-based retrieval workflows. Good understanding of prompt engineering , including system prompts, RAG workflows, and evaluation techniques. Experience handling JSON schemas, logging, asynchronous programming, and error handling . Foundational knowledge of machine learning concepts such as preprocessing, evaluation, and deployment. Exposure to cloud platforms (AWS, GCP, or Azure) is an advantage. Nice-to-Have Experience with vector databases such as Pinecone, Weaviate, or FAISS. Familiarity with frameworks like LangChain, LlamaIndex , or similar tools. Understanding of CI/CD pipelines and modern DevOps practices. Exposure to monitoring, observability, and model performance tracking tools. Basic knowledge of Docker and containerized deployments. Qualifications Bachelor’s degree in Computer Science, Engineering , or a related field. 2–3 years of professional experience in software or machine learning engineering roles, preferably in a product-based environment. Skills Python, LLMs, REST APIs, FastAPI, Flask, Machine Learning, Prompt Engineering

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