AI/ML Engineer

Weekday AI; Bengaluru; On-site; INR 3,000,000 - 4,000,000; Full time

Job summary

Weekday AI in Bengaluru, India, is seeking an AI/ML Engineer to build scalable AI systems focused on LLMs, RAG, and personalization. You will design retrieval architectures, adapt open-source models, and craft domain-specific capabilities enabling context-aware user experiences.

The ideal candidate combines Python expertise with hands-on experience in LLM engineering, vector databases, memory systems, and production deployment, driving performance and reliability at scale.

Qualifications

• 3+ years of experience in AI/ML engineering, NLP, or production AI development.
• Strong Python proficiency and production-grade AI system experience.
• Hands-on with LLMs, open-source models, embeddings, and vector databases.
• Experience in retrieval, memory, context management, or personalisation systems.
• Familiarity with LLM orchestration, prompt engineering, and multi-step AI pipelines.

Responsibilities

• Design and build conversational AI with contextual understanding and memory.
• Develop RAG pipelines using embeddings, databases, and knowledge retrieval.
• Create memory systems for profiling, relevance scoring, retrieval, and ranking.
• Integrate and fine-tune open-source LLMs for product/domain needs.
• Develop domain-specific datasets and training pipelines for fine-tuning.
• Implement LLM orchestration, prompt engineering, and lightweight ML workflows.
• Build evaluation frameworks for retrieval relevance and model performance.
• Optimize inference latency, compute use, and deployment costs.

Skills

Python

LLMs

RAG pipelines

Vector databases

Model deployment

NLP

Prompt engineering

GPUs inference

Tools

Neo4j

Job description

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗥𝘀 𝟯𝟬𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟰𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟯𝟬-𝟰𝟬 𝗟𝗣𝗔)

Experience: 3+ yrs

Location: Bengaluru

We are looking for a skilled and innovativeAI/ML Engineerto build and scale intelligent AI systems focused onLarge Language Models (LLMs), conversational AI, long-term memory, and personalization. The role involves developing advanced retrieval systems, adapting open-source models, and building domain-specific AI capabilities that deliver context-aware, accurate, and personalized user experiences.

The ideal candidate will combine strong Python programming skills with hands-on expertise in LLM engineering, Retrieval-Augmented Generation (RAG), vector databases, model fine-tuning, and production deployment.

Key Responsibilities

• Design and develop conversational AI systems with contextual understanding, long-term memory, and personalised response generation.
• Build and optimiseRAG pipelinesusing embeddings, vector databases, structured data, and knowledge retrieval techniques.
• Develop memory systems for user profiling, information extraction, relevance scoring, retrieval, ranking, and context selection.
• Integrate, deploy, and fine-tune open-source LLMs for specific product and domain requirements.
• Develop specialised Small Language Models (SLMs) to support domain-specific reasoning and improve response quality.
• Create domain-specific datasets and training pipelines for fine-tuning, model adaptation, and continuous learning.
• Implement LLM orchestration, prompt engineering, classification, ranking, and lightweight machine learning workflows.
• Build evaluation frameworks to measure retrieval relevance, memory accuracy, reasoning quality, response consistency, and overall model performance.
• Optimise inference latency, compute utilisation, model reliability, and deployment costs.
• Develop and maintain model-serving APIs, automated tests, monitoring systems, and production deployment workflows.
• Investigate AI failures, analyse model outputs, and implement improvements based on measurable performance indicators.
• Collaborate with Product and Engineering teams to translate user requirements into scalable AI solutions.
• Evaluate emerging LLM technologies, retrieval architectures, and model optimisation techniques to improve system capabilities.

What Makes You a Great Fit

• 3+ years of experiencein AI/ML engineering, machine learning, NLP, or production AI application development.
• Strong proficiency inPythonand experience building reliable, production-grade AI systems.
• Hands-on experience with LLMs, open-source models, embeddings, vector databases, and RAG architectures.
• Understanding of LLM fine-tuning, dataset preparation, model evaluation, and inference optimisation.
• Experience developing retrieval, conversational memory, context management, or personalisation systems.
• Familiarity with LLM orchestration frameworks, prompt engineering, and multi-step AI pipelines.
• Experience deploying models through APIs and implementing testing, monitoring, and production support.
• Strong analytical and problem-solving skills, with the ability to evaluate AI improvements using measurable outcomes.
• Knowledge ofLoRA/QLoRA, SLMs, knowledge graphs, Neo4j, or graph-based retrievalis an advantage.
• Experience with recommendation systems, ranking models, synthetic data generation, or automated evaluation pipelines is beneficial.
• Familiarity with GPU inference optimisation and self-hosted open-source models is desirable.
• Experience in astrology, symbolic reasoning, or specialised domain-reasoning systems is an added advantage.
• Strong ownership, communication, and collaboration skills, with the ability to drive AI initiatives from experimentation through production.