ML Engineer - II

Weekday AI; India; On-site; INR 2,000,000 - 3,500,000; Full time

Job summary

Weekday AI in Bengaluru is seeking an experienced AI/ML Engineer to own end-to-end ML/AI systems. The role focuses on building intelligent applications using LLMs, RAG, conversational AI, and agentic workflows.

The ideal candidate combines strong Python and software fundamentals with hands-on experience deploying AI systems in real production environments. Join a team delivering scalable AI-powered experiences.

Qualifications

• 3+ years of experience in ML, Applied ML, NLP, Generative AI, or AI engineering.
• Strong Python proficiency with software engineering fundamentals.
• Hands-on experience building, deploying, and optimizing AI systems in production.
• Experience with LLMs, embeddings, and retrieval techniques for real-world apps.
• Familiarity with LangChain/LangGraph and vector databases is a plus.

Responsibilities

• Design, build, and own production-grade ML/AI systems across the lifecycle.
• Develop LLM-powered apps, including RAG pipelines and conversational AI.
• Create retrieval systems using embeddings, vector search, and context enrichment.
• Develop personalization, memory, recommendations, and user intelligence features.
• Define LLM orchestration workflows coordinating models, tools, and retrieval layers.
• Build evaluation frameworks for LLM quality, reliability, latency, and cost.
• Optimize AI systems for production performance and scalability.
• Collaborate with product and backend teams to deploy AI-powered features.
• Evaluate emerging LLMs, open-source models, and AI tooling.

Skills

Python

LLMs

RAG

Vector search

Conversational AI

ML/AI systems

LLM orchestration

LangChain

LangGraph

Pinecone

Weaviate

Milvus

pgvector

Hugging Face

Tools

LangChain

LangGraph

Pinecone

Weaviate

Milvus

pgvector

Hugging Face

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 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.