Senior AI Engineer

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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.