Job Description:

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 mitigation.

AI Engineer