ML Engineer

Weekday AI (YC W21); Bengaluru; On-site; INR 5,000,000 - 15,000,000; Full time

Job summary

Weekday AI (YC W21) is seeking a talented Machine Learning Engineer based in Bengaluru to design, develop, and deploy production-grade ML systems with a strong focus on LLMs.

You will work on fast-moving AI products, implement training and fine-tuning workflows, build robust data pipelines, and collaborate with product and engineering teams to translate business goals into scalable ML solutions.

Qualifications

• 1–8 years in ML/AI or data science.
• Hands-on experience with LLMs and generative AI.
• Experience with Python and ML frameworks.

Responsibilities

• Design, develop and deploy ML models and AI-powered applications with a primary focus on LLM-based solutions.
• Work with pre-trained language models for text generation, classification, summarization, information extraction, QA, and conversational AI.
• Fine-tune and optimize LLMs using supervised fine-tuning, LoRA, and related approaches.
• Develop robust data pipelines for collecting, cleaning, preprocessing, and preparing datasets for model training and evaluation.
• Experiment with model architectures, prompting strategies, embeddings, retrieval techniques, and inference approaches.
• Build and maintain evaluation frameworks to measure model quality, latency, and reliability.
• Collaborate with product, engineering, and data teams to translate business requirements into ML solutions.
• Optimize models for production environments considering inference cost and scalability.
• Monitor deployed models and improve performance based on feedback.

Skills

LLMs

Python

NLP

Model evaluation

Optimization

Debugging

Tools

PyTorch

TensorFlow

Hugging Face

LLM APIs

Job description

Job Description

This role is for one of Weekday’s clients

Salary range: Rs 5000000 - Rs 15000000 (ie INR 50 - 150 LPA)

Min Experience: 1+ years

Location: Bengaluru, Karnataka

Job Type: full-time

We are looking for a talented Machine Learning Engineer with 1–8 years of experience to design, develop, and deploy intelligent machine learning systems, with a strong focus on Large Language Models (LLMs). You will work on building production-grade AI solutions, improving model performance, and integrating advanced language-model capabilities into scalable products.

The ideal candidate combines strong machine learning fundamentals with hands-on experience working with LLMs, model training or fine-tuning, inference, evaluation, and AI application development.

Key Responsibilities

• Design, develop, and deploy machine learning models and AI-powered applications with a primary focus on LLM-based solutions.
• Work with pre-trained language models for tasks such as text generation, classification, summarization, information extraction, question answering, and conversational AI.
• Fine-tune and optimize LLMs using techniques such as supervised fine-tuning, parameter-efficient fine-tuning, LoRA, and related approaches.
• Develop robust data pipelines for collecting, cleaning, preprocessing, and preparing datasets for model training and evaluation.
• Experiment with model architectures, prompting strategies, embeddings, retrieval techniques, and inference approaches to improve system performance.
• Build and maintain evaluation frameworks to measure model quality, accuracy, relevance, latency, and reliability.
• Collaborate with product, engineering, and data teams to translate business requirements into scalable machine learning solutions.
• Optimize models for production environments, considering inference cost, latency, scalability, and resource utilization.
• Monitor deployed models and continuously improve their performance based on real-world feedback and evaluation results.
• Stay current with advances in LLMs, generative AI, machine learning research, and emerging AI engineering practices.

Must-Have Skills

• 1–8 years of professional experience in Machine Learning, AI, Data Science, or a related field.
• Strong hands-on experience with Large Language Models (LLMs) and generative AI.
• Strong understanding of machine learning concepts, algorithms, model evaluation, and optimization.
• Experience with Python and commonly used machine learning frameworks and libraries.
• Understanding of NLP concepts, transformer architectures, embeddings, tokenization, and model inference.
• Experience working with LLM APIs, open-source language models, or enterprise AI platforms.
• Ability to design, experiment with, evaluate, and productionize ML/LLM solutions.
• Strong analytical, problem-solving, and debugging skills.

Good-to-Have Skills

• Experience working with foundational models and open-source models such as Llama, Mistral, Gemma, or similar architectures.
• Experience with model fine-tuning, quantization, distillation, and parameter-efficient training techniques.
• Knowledge of RAG architectures, vector databases, semantic search, and embedding models.
• Familiarity with PyTorch, TensorFlow, Hugging Face Transformers, or similar frameworks.
• Experience with distributed training, GPU optimization, or high-performance inference.
• Knowledge of MLOps, model deployment, monitoring, and cloud-based ML infrastructure.
• Familiarity with prompt engineering, AI agents, multimodal models, or reinforcement learning from human feedback.