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 strongย Generative AI / Machine Learning Engineerย 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 inย 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 usingย 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 asย 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 usingย 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 asย XGBoost and Random Forestย 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
- 2โ5 years of experienceย in AI/ML engineering, Generative AI, or a related software engineering field.
- Strong programming skills inย Pythonย and hands-on experience with LLM frameworks.
- Proven experience buildingย 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 withย LoRA/PEFT, quantisation, GPU-based training, or LLM inference optimisationย 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.