We are looking for a hands-on AI / Machine Learning Engineer who can design, build, and deploy intelligent systems using contemporary ML and LLM-based architectures. You will work closely with product, engineering, and business teams to deliver scalable AI solutions for real-world use cases.

This role is ideal for someone who enjoys both research + production engineering, and has experience taking models from experimentation to deployment.

Key Responsibilities
- Design, develop, and deploy machine learning and AI solutions for production use
- Build and optimize LLM-based systems (RAG, agents, prompt pipelines)
- Develop data pipelines for training, fine-tuning, and inference:
- Implement vector search, embeddings, and retrieval systems:
- Evaluate model performance and continuously improve accuracy and latency
- Work with backend engineers to integrate AI services via APIs
- Write clean, scalable, and well-documented code
- Stay updated with the latest advancements in AI/ML and LLMs

Required SkillsCore
- Strong proficiency in Python:




- Solid understanding of Machine Learning fundamentals (supervised, unsupervised, evaluation metrics)
- Experience with deep learning frameworks: PyTorch or TensorFlow
- Experience building REST APIs (FastAPI / Flask)

LLM & AI Systems
- Hands-on experience with LLMs (OpenAI, Azure OpenAI, Anthropic, etc.)
- Experience with RAG architectures:
- Vector databases:Chroma, Pinecone, Weaviate, FAISS:
- Prompt engineering and structured outputs (JSON / schemas)

Data & Infra
- Experience with SQL / NoSQL databases:
- Familiarity with cloud platforms (AWS / GCP / Azure)
- Understanding of model deployment, monitoring, and scaling:
- Experience with Docker (preferred)

Good to Have
- Experience with LangChain, LangGraph, LlamaIndex, or similar frameworks:
- Experience with multi-agent systems:
- Familiarity with ML observability tools (Langfuse, LangSmith, Weights & Biases)
- Knowledge of NLP, computer vision, or recommendation systems:
- Experie

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