Weekday AI·3 days ago
3 days ago
Machine Learning Engineer - 2
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What you'll do
- Design, develop, and own production-grade ML/AI systems across the complete development lifecycle.
- Build and integrate LLM-powered applications, including RAG pipelines, conversational AI, and agentic workflows.
- Develop retrieval systems using embeddings, vector search, semantic retrieval, and context enrichment.
- Build AI capabilities for personalization, memory, recommendations, and user intelligence.
- Design LLM orchestration workflows to coordinate models, tools, retrieval systems, and application logic.
- Develop evaluation frameworks to measure LLM quality, accuracy, relevance, reliability, latency, and cost.
- Optimize AI systems for production performance, scalability, response quality, and resource efficiency.
- Combine structured domain intelligence with ML, retrieval, and LLM reasoning to deliver context-aware outputs.
- Build and maintain APIs and production services that integrate AI capabilities with backend systems.
- Design scalable ML/AI architectures suitable for high-volume production environments.
- Develop experiments, prototypes, and proof-of-concepts and transition successful solutions into production.
- Implement monitoring, evaluation, debugging, and continuous improvement processes for deployed AI systems.
What they're looking for
- 3+ years of experience in Machine Learning, Applied ML, NLP, Generative AI, or AI engineering.
- Strong proficiency in Python with solid software engineering and programming fundamentals.
- Hands-on experience building applications using LLMs, RAG, embeddings, vector search, or conversational AI.
- Proven experience deploying and supporting ML/AI systems in production.
- Strong understanding of machine learning fundamentals, model evaluation, experimentation, and performance optimization.
- Experience designing and developing AI APIs, scalable services, and production-ready systems.
- Strong understanding of system design, scalability, reliability, and cloud-based application development.
- Experience evaluating and optimizing LLM applications for quality, latency, cost, and reliability.
- Strong understanding of retrieval pipelines, prompt engineering, context management, and LLM orchestration.
- Ability to independently own technical problems across the complete lifecycle: design → build → evaluate → deploy → improve.
Nice to have
- Experience with LangChain or LangGraph is an advantage.
- Familiarity with vector databases and technologies such as Pinecone, Weaviate, Milvus, pgvector, or similar is desirable.
- Experience with Hugging Face and open-source LLMs is a plus.
- Knowledge of MLOps, LLM evaluation frameworks, recommendation systems, or multilingual/Indic NLP is an advantage.
Summarised by NextRaise from the employer’s description, which follows in full below.
Full description from employer
𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀
𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟮𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟯𝟱𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟮𝟬-𝟯𝟱 𝗟𝗣𝗔)
Experience: 3+ yrs
Location: Bengaluru
Job Type: Full-time
We are looking for an experienced AI/ML Engineer to build and own production-grade Machine Learning and Generative AI systems end-to-end. The role focuses on developing intelligent applications using LLMs, RAG, conversational AI, agentic workflows, personalization, recommendations, memory, and user intelligence.
The ideal candidate will combine strong Python and software engineering fundamentals with hands-on experience building, evaluating, deploying, and optimizing AI systems for real-world applications. You will work across ML, retrieval, LLM orchestration, and scalable backend systems to deliver reliable and impactful AI-powered experiences.
Requirements
Key Responsibilities
- Design, develop, and own production-grade ML/AI systems across the complete development lifecycle.
- Build and integrate LLM-powered applications, including RAG pipelines, conversational AI, and agentic workflows.
- Develop retrieval systems using embeddings, vector search, semantic retrieval, and context enrichment.
- Build AI capabilities for personalization, memory, recommendations, and user intelligence.
- Design LLM orchestration workflows to coordinate models, tools, retrieval systems, and application logic.
- Develop evaluation frameworks to measure LLM quality, accuracy, relevance, reliability, latency, and cost.
- Optimize AI systems for production performance, scalability, response quality, and resource efficiency.
- Combine structured domain intelligence with ML, retrieval, and LLM reasoning to deliver context-aware outputs.
- Build and maintain APIs and production services that integrate AI capabilities with backend systems.
- Design scalable ML/AI architectures suitable for high-volume production environments.
- Develop experiments, prototypes, and proof-of-concepts and transition successful solutions into production.
- Implement monitoring, evaluation, debugging, and continuous improvement processes for deployed AI systems.
- Collaborate with Product, Backend, and cross-functional engineering teams to deliver AI-powered features.
- Evaluate emerging LLMs, open-source models, retrieval techniques, agent frameworks, and AI tooling.
- Contribute to engineering standards, technical documentation, model evaluation practices, and AI system design.
- Take ownership of problems end-to-end, from design and implementation through evaluation, deployment, and production support.
What Makes You a Great Fit
- 3+ years of experience in Machine Learning, Applied ML, NLP, Generative AI, or AI engineering.
- Strong proficiency in Python with solid software engineering and programming fundamentals.
- Hands-on experience building applications using LLMs, RAG, embeddings, vector search, or conversational AI.
- Proven experience deploying and supporting ML/AI systems in production.
- Strong understanding of machine learning fundamentals, model evaluation, experimentation, and performance optimization.
- Experience designing and developing AI APIs, scalable services, and production-ready systems.
- Strong understanding of system design, scalability, reliability, and cloud-based application development.
- Experience evaluating and optimizing LLM applications for quality, latency, cost, and reliability.
- Strong understanding of retrieval pipelines, prompt engineering, context management, and LLM orchestration.
- Ability to independently own technical problems across the complete lifecycle: design → build → evaluate → deploy → improve.
- Experience with LangChain or LangGraph is an advantage.
- Familiarity with vector databases and technologies such as Pinecone, Weaviate, Milvus, pgvector, or similar is desirable.
- Experience with Hugging Face and open-source LLMs is a plus.
- Knowledge of MLOps, LLM evaluation frameworks, recommendation systems, or multilingual/Indic NLP is an advantage.
- Strong analytical and problem-solving skills with a practical, experimentation-driven approach.
- Excellent communication and collaboration skills with the ability to work effectively across Product and Engineering teams.
- Strong ownership mindset and interest in building reliable, scalable, and user-focused AI products.
Company
Weekday AI
Bengaluru, India
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