Machine Learning Engineer - 2
At a glance
Mid-level Machine Learning Engineering role at Weekday AI. Bengaluru ยท full-time.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and run machine learning and generative AI systems from initial design through deployment and ongoing support
- Create LLM applications spanning retrieval-augmented generation, conversational interfaces, and agent-based workflows
- Use embeddings and vector search to retrieve relevant information, enrich context, and support personalization and recommendations
- Measure response quality, accuracy, speed, reliability, and cost, then improve systems for production use
- Develop APIs and scalable backend services, and move successful experiments into production with engineering teams
What you bring
- At least three years working in machine learning, applied ML, NLP, generative AI, or AI engineering
- Strong Python skills and solid software development fundamentals
- Practical experience building LLM applications using RAG, embeddings, vector search, or conversational AI
- Experience deploying and maintaining production AI systems, including evaluation and performance improvements
Who this fits
This mid-level, full-time role suits an engineer who can take AI systems through design, development, evaluation, deployment, and improvement. Youโll work with Product and Engineering teams. The role is based onsite in Bengaluru.
From the employer
๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐
๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฎ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฏ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฎ๐ฌ-๐ฏ๐ฑ ๐๐ฃ๐)
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 โ build โ evaluate โ deploy โ 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.