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.

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.