ML Engineer - II

Weekday 1; Bengaluru; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

Weekday 1 in Bengaluru is seeking an experienced AI/ML Engineer to build and own production-grade ML/AI systems end-to-end. You will work on LLM orchestration, RAG pipelines, and scalable backend integration, delivering personalized and memory-enabled AI experiences.

The role requires strong Python software engineering fundamentals, hands-on experience with LLMs, embeddings, and retrieval pipelines, and a proven track record of deploying AI systems in production.

Qualifications

• 3+ years of experience in Machine Learning, Applied ML, NLP, Generative AI, or AI engineering.
• Strong proficiency in Python with solid software engineering 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.
• Understanding of ML fundamentals, model evaluation, experimentation, and performance optimization.
• Experience designing AI APIs, scalable services, and production-ready systems.
• Knowledge of system design, scalability, reliability, and cloud-based development.
• Experience evaluating LLM applications for quality, latency, cost, and reliability.
• Understanding retrieval pipelines, prompt engineering, context management, and LLM orchestration.
• Ability to own problems end-to-end from design to production support.

Responsibilities

• Design, develop, and own production-grade ML/AI systems across the full lifecycle.
• Build and integrate LLM-powered applications including RAG pipelines and conversational AI.
• 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, and app logic.
• Develop evaluation frameworks to measure LLM quality, accuracy, relevance, and latency.
• Optimize AI systems for production performance and scalability.
• Combine structured domain intelligence with ML, retrieval, and LLM reasoning for outputs.
• Build and maintain APIs and production services integrating AI with backend systems.
• Design scalable ML/AI architectures for high-volume production environments.
• Develop experiments, prototypes, and transition successful solutions to production.
• Implement monitoring, debugging, & continuous improvement for deployed AI systems.
• Collaborate with Product, Backend, and cross-functional teams to deliver AI features.
• Evaluate emerging LLMs, open-source models, and retrieval techniques.
• Contribute to engineering standards, documentation, and model evaluation practices.
• Take ownership end-to-end from design to deployment and production support.

Skills

Python

Machine Learning

NLP

Generative AI

LLMs

RAG

Embeddings

Vector search

Conversational AI

APIs

LangChain

LangGraph

Pinecone

Weaviate

Milvus

pgvector

Hugging Face

MLOps

Multilingual NLP

Tools

LangChain

LangGraph

Pinecone

Weaviate

Milvus

pgvector

Job description

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 experiencedAI/ML Engineerto build and own production-gradeMachine Learning and Generative AI systemsend-to-end. The role focuses on developing intelligent applications usingLLMs, RAG, conversational AI, agentic workflows, personalization, recommendations, memory, and user intelligence.

The ideal candidate will combine strongPython and software engineering fundamentalswith 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 ownproduction-grade ML/AI systemsacross the complete development lifecycle.
• Build and integrateLLM-powered applications, including RAG pipelines, conversational AI, and agentic workflows.
• Develop retrieval systems usingembeddings, vector search, semantic retrieval, and context enrichment.
• Build AI capabilities forpersonalization, memory, recommendations, and user intelligence.
• Design LLM orchestration workflows to coordinate models, tools, retrieval systems, and application logic.
• Develop evaluation frameworks to measureLLM quality, accuracy, relevance, reliability, latency, and cost.
• Optimize AI systems for production performance, scalability, response quality, and resource efficiency.
• Combine structured domain intelligence withML, retrieval, and LLM reasoningto 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 emergingLLMs, 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, fromdesign and implementation through evaluation, deployment, and production support.

What Makes You a Great Fit

• 3+ years of experiencein Machine Learning, Applied ML, NLP, Generative AI, or AI engineering.
• Strong proficiency inPythonwith solid software engineering and programming fundamentals.
• Hands-on experience building applications usingLLMs, RAG, embeddings, vector search, or conversational AI.
• Proven experience deploying and supportingML/AI systems in production.
• Strong understanding of machine learning fundamentals, model evaluation, experimentation, and performance optimization.
• Experience designing and developingAI 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 forquality, 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 withLangChain or LangGraphis an advantage.
• Familiarity with vector databases and technologies such asPinecone, Weaviate, Milvus, pgvector, or similaris desirable.
• Experience withHugging Face and open-source LLMsis a plus.
• Knowledge ofMLOps, LLM evaluation frameworks, recommendation systems, or multilingual/Indic NLPis 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.