๐ง๐ต๐ถ๐ ๐ฟ๐ผ๐น๐ฒ ๐ถ๐ ๐ณ๐ผ๐ฟ ๐ผ๐ป๐ฒ ๐ผ๐ณ ๐๐ต๐ฒ ๐ช๐ฒ๐ฒ๐ธ๐ฑ๐ฎ๐'๐ ๐ฐ๐น๐ถ๐ฒ๐ป๐๐ ๐ฆ๐ฎ๐น๐ฎ๐ฟ๐ ๐ฟ๐ฎ๐ป๐ด๐ฒ: ๐ฅ๐ ๐ฎ๐ฑ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ - ๐ฅ๐ ๐ฐ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ๐ฌ (๐ถ๐ฒ ๐๐ก๐ฅ ๐ฎ๐ฑ-๐ฐ๐ฌ ๐๐ฃ๐) 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 โข Design and develop LLM-powered applications and autonomous or semi-autonomous AI agents. โข Build agentic workflows using LangChain, LangGraph, LlamaIndex, or similar frameworks. โข Implement function calling, tool integration, and multi-step reasoning workflows using approaches such as ReAct. โข Develop end-to-end AI systems that can plan, execute tasks, and interact with external tools and APIs. 2. Retrieval-Augmented Generation (RAG) โข Design and implement scalable RAG pipelines for document-based knowledge retrieval and contextual response generation. โข Work with vector databases such as Pinecone, FAISS, and Milvus. โข Optimise document chunking, embedding generation, indexing, and retrieval strategies. โข Evaluate retrieval quality, improve response relevance, and implement techniques to minimise LLM hallucinations. 3. LLM Optimisation and Fine-Tuning โข Experiment with prompt engineering, context management, and response-quality optimisation. โข Fine-tune models using LoRA, PEFT, and related parameter-efficient techniques where appropriate. โข Apply quantisation techniques, including 8-bit and 4-bit quantisation, to improve inference efficiency. โข Develop an understanding of model alignment approaches, including RLHF. 4. Machine Learning and Deep Learning โข Apply machine learning techniques to structured and unstructured datasets. โข Demonstrate an understanding of neural networks, backpropagation, regularisation, and model evaluation. โข Work with algorithms such as XGBoost and Random Forest for tabular prediction tasks. โข Understand foundational architectures such as RNNs, LSTMs, and CNNs where relevant. โข Train and optimise models using GPU/TPU-based environments when required. 5. Deployment and MLOps โข Build production-ready AI and ML pipelines with appropriate testing, deployment, and monitoring. โข Track model performance and identify data drift, concept drift, and quality degradation. โข Collaborate with engineering teams to integrate AI capabilities into applications and services. โข Improve reliability, scalability, latency, and resource utilisation across deployed AI systems. What Makes You a Great Fit โข 2โ5 years of experience in AI/ML engineering, Generative AI, or a related software engineering field. โข Strong programming skills in Python and hands-on experience with LLM frameworks. โข Proven experience building RAG pipelines, AI agents, and tool-using systems. โข Good understanding of vector databases, embeddings, retrieval optimisation, and prompt engineering. โข Knowledge of practical techniques for evaluating LLM outputs and mitigating hallucinations. โข Solid fundamentals in machine learning, deep learning, model training, and evaluation metrics such as precision and recall. โข Familiarity with class imbalance, model validation, and performance optimisation. โข Experience with LoRA/PEFT, quantisation, GPU-based training, or LLM inference optimisation is an advantage. โข Exposure to MLOps, model deployment, monitoring, and real-time AI applications is desirable. โข Strong analytical, debugging, and problem-solving skills. โข Ability to translate complex requirements into practical AI solutions and collaborate effectively with cross-functional teams. โข A proactive learning mindset and genuine interest in emerging AI technologies and production-grade AI systems.