# Senior AI Engineer

> Weekday AI · Bengaluru, India · Full-time · Posted 2026-10-12

**Salary:** INR 2,500,000–4,000,000

**Workplace:** on_site

**Department:** Weekday's Client via platform

## Description

𝗧𝗵𝗶𝘀 𝗿𝗼𝗹𝗲 𝗶𝘀 𝗳𝗼𝗿 𝗼𝗻𝗲 𝗼𝗳 𝘁𝗵𝗲 𝗪𝗲𝗲𝗸𝗱𝗮𝘆'𝘀 𝗰𝗹𝗶𝗲𝗻𝘁𝘀

𝗦𝗮𝗹𝗮𝗿𝘆 𝗿𝗮𝗻𝗴𝗲: 𝗥𝘀 𝟮𝟱𝟬𝟬𝟬𝟬𝟬 - 𝗥𝘀 𝟰𝟬𝟬𝟬𝟬𝟬𝟬 (𝗶𝗲 𝗜𝗡𝗥 𝟮𝟱-𝟰𝟬 𝗟𝗣𝗔)

Experience: 2+ yrs

Location: Bengaluru

Job Type: Full-time

We are looking for a technically strong **Generative AI / Machine Learning Engineer** to design, develop, and deploy intelligent applications powered by Large Language Models (LLMs), AI agents, and machine learning systems. The ideal candidate will have hands-on experience in **Python, agentic AI, Retrieval-Augmented Generation (RAG), LLM frameworks, and production-ready AI engineering**.

This role is suited for an engineer who enjoys solving complex problems, experimenting with emerging AI technologies, and transforming research concepts into scalable, reliable, and high-performing applications.

## Requirements

Key Responsibilities

### 1\. Generative AI and Agent Development

-   Design and develop LLM-powered applications and autonomous or semi-autonomous AI agents.
-   Build agentic workflows using **LangChain, LangGraph, LlamaIndex**, or similar frameworks.
-   Implement function calling, tool integration, and multi-step reasoning workflows using approaches such as ReAct.
-   Develop end-to-end AI systems that can plan, execute tasks, and interact with external tools and APIs.

### 2\. Retrieval-Augmented Generation (RAG)

-   Design and implement scalable RAG pipelines for document-based knowledge retrieval and contextual response generation.
-   Work with vector databases such as **Pinecone, FAISS, and Milvus**.
-   Optimise document chunking, embedding generation, indexing, and retrieval strategies.
-   Evaluate retrieval quality, improve response relevance, and implement techniques to minimise LLM hallucinations.

### 3\. LLM Optimisation and Fine-Tuning

-   Experiment with prompt engineering, context management, and response-quality optimisation.
-   Fine-tune models using **LoRA, PEFT**, and related parameter-efficient techniques where appropriate.
-   Apply quantisation techniques, including 8-bit and 4-bit quantisation, to improve inference efficiency.
-   Develop an understanding of model alignment approaches, including RLHF.

### 4\. Machine Learning and Deep Learning

-   Apply machine learning techniques to structured and unstructured datasets.
-   Demonstrate an understanding of neural networks, backpropagation, regularisation, and model evaluation.
-   Work with algorithms such as **XGBoost and Random Forest** for tabular prediction tasks.
-   Understand foundational architectures such as RNNs, LSTMs, and CNNs where relevant.
-   Train and optimise models using GPU/TPU-based environments when required.

### 5\. Deployment and MLOps

-   Build production-ready AI and ML pipelines with appropriate testing, deployment, and monitoring.
-   Track model performance and identify data drift, concept drift, and quality degradation.
-   Collaborate with engineering teams to integrate AI capabilities into applications and services.
-   Improve reliability, scalability, latency, and resource utilisation across deployed AI systems.

What Makes You a Great Fit

-   **2–5 years of experience** in AI/ML engineering, Generative AI, or a related software engineering field.
-   Strong programming skills in **Python** and hands-on experience with LLM frameworks.
-   Proven experience building **RAG pipelines, AI agents, and tool-using systems**.
-   Good understanding of vector databases, embeddings, retrieval optimisation, and prompt engineering.
-   Knowledge of practical techniques for evaluating LLM outputs and mitigating hallucinations.
-   Solid fundamentals in machine learning, deep learning, model training, and evaluation metrics such as precision and recall.
-   Familiarity with class imbalance, model validation, and performance optimisation.
-   Experience with **LoRA/PEFT, quantisation, GPU-based training, or LLM inference optimisation** is an advantage.
-   Exposure to MLOps, model deployment, monitoring, and real-time AI applications is desirable.
-   Strong analytical, debugging, and problem-solving skills.
-   Ability to translate complex requirements into practical AI solutions and collaborate effectively with cross-functional teams.
-   A proactive learning mindset and genuine interest in emerging AI technologies and production-grade AI systems.

## Apply

[Apply at Weekday AI](https://apply.workable.com/weekday-1/j/FEF87A553C/apply)

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