AI Engineer – Machine Learning & Generative AI

About the role

We are looking for a skilled AI Engineer to design, build, and deploy machine learning, generative AI, and agentic systems that address real‑world business challenges. The role can be based in India or China and involves close collaboration with data scientists, software engineers, and product teams.

Key responsibilities

• Design, develop, and productionize machine learning and AI‑driven models.
• Build and maintain scalable data pipelines for training, evaluation, and inference.
• Fine‑tune large language models (LLMs) and optimize deep‑learning architectures for performance and cost.
• Implement Retrieval‑Augmented Generation (RAG) pipelines, including document chunking, embedding generation, and hybrid retrieval strategies.
• Manage vector databases such as Pinecone, Weaviate, Milvus, Qdrant, pgvector, or FAISS for similarity search at scale.
• Develop agentic AI workflows using frameworks like LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, or Semantic Kernel.
• Orchestrate long‑running agentic processes with Temporal, Airflow, or Step Functions.
• Integrate AI/ML models into applications via APIs, microservices, or embedded systems.
• Apply MLOps/LLMOps practices: CI/CD, prompt/version management, monitoring, and automated retraining.
• Conduct experiments, A/B tests, and evaluate model quality (hallucination, relevance, groundedness).
• Ensure responsible AI practices, including bias mitigation, explainability, and data privacy compliance.

Required profile

• Strong background in machine learning, deep learning, and large language models.
• Experience delivering AI solutions from prototype to production.
• Ability to work cross‑functionally with product, engineering, and data science teams.
• Familiarity with responsible AI principles and performance evaluation.

Required skills

• Python programming.
• Machine learning frameworks (e.g., PyTorch, TensorFlow).
• Large language model fine‑tuning and optimization.
• Retrieval‑Augmented Generation techniques.
• Vector database technologies: Pinecone, Weaviate, Milvus, Qdrant, pgvector, FAISS.
• Agentic AI frameworks: LangChain, LangGraph, LlamaIndex, CrewAI, AutoGen, Semantic Kernel.
• Orchestration tools: Temporal, Airflow, Step Functions.
• MLOps/LLMOps practices: CI/CD pipelines, model monitoring, automated retraining.
• API and microservice integration.