Location Bangalore

Experience 7-14 Years

Employment Type Full time

ABOUT THE ROLE

We are seeking a seasoned AI Engineer to build, fine-tune, and deploy intelligent AI systems at scale. You will

work at the intersection of LLMs, machine learning, and software engineering - developing production-ready AI

features and pipelines that power our core product.

KEY RESPONSIBILITIES

- Design, develop, and deploy AI/ML models and pipelines in production environments

- Implement Retrieval-Augmented Generation (RAG) architectures and agentic AI workflows

- Fine-tune and optimize LLMs for domain-specific use cases using RLHF, LoRA, QLoRA

- Build robust prompt engineering frameworks and evaluation pipelines

- Integrate LLM APIs (OpenAI, Claude, Gemini) and open-source models into product features

- Develop and maintain vector search infrastructure and embedding pipelines

- Collaborate with architects, backend engineers, and product teams on AI feature delivery

- Monitor model performance, conduct A/B testing, and iterate based on metrics

- Implement guardrails, safety layers, and hallucination-mitigation strategies

- Contribute to MLOps practices: model versioning, deployment pipelines, monitoring

KEY SKILLS REQUIREMENTS

- Strong expertise in Python, with deep knowledge of AI/ML libraries (PyTorch, TensorFlow, HuggingFace

Transformers)



- Hands-on experience with LLM APIs and prompt engineering techniques (CoT, few-shot, ReAct) - Experience with RAG systems, embedding models (text-embedding-3, BGE, Cohere), and vector stores

- Knowledge of agentic frameworks: LangChain, LlamaIndex, AutoGen, CrewAI, or Semantic Kernel - Familiarity with fine-tuning techniques: LoRA, QLoRA, PEFT, instruction tuning

- Experience deploying models on cloud platforms (AWS SageMaker, GCP Vertex AI, Azure ML) - Understanding of data preprocessing, feature engineering, and model evaluation metrics

- Proficiency with MLOps tools: MLflow, DVC, Weights Biases, BentoML

- Experience with containerization and orchestration: Docker, Kubernetes

- Strong debugging and experimentation skills with Jupyter, FastAPI, Streamlit

NICE TO HAVE

- Experience with multi-modal models (vision-language models, Whisper, DALL-E) - Published papers or Kaggle/competition achievements

- Exposure to speech AI, computer vision, or NLP specializations

- Knowledge of responsible AI, fairness metrics, and bias

Disclaimer: This job description has been sourced from a public domain and may have been modified by Naukri.com to improve clarity for our users. We encourage job seekers to verify all details directly with the employer via their official channels before applying.

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