AI Engineer

Job Description

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

Are you interested in this position?
Apply by clicking on the “Apply Now” button below!
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