Generative AI engineer

Key Responsibilities Design, develop, and deploy AI-powered applications using Large Language Models (LLMs). Build and optimize Retrieval-Augmented Generation (RAG) pipelines for knowledge-based AI applications. Develop effective prompt engineering strategies to improve AI model performance and output quality. Integrate vector databases for semantic search and contextual information retrieval. Fine-tune, evaluate, and optimize foundation models where applicable. Build AI APIs and services that integrate seamlessly with web and enterprise applications. Collaborate with AI/ML engineers, software developers, and product teams to deliver production-ready AI solutions. Implement AI orchestration workflows using frameworks such as LangChain, LlamaIndex, or similar. Monitor AI application performance, latency, and response quality in production environments. Ensure responsible AI practices, data privacy, security, and compliance throughout the AI lifecycle. Document AI architectures, prompt libraries, workflows, and deployment processes. Stay updated with advancements in Generative AI, LLMs, AI agents, and emerging technologies. Required Qualifications Bachelor's degree in Computer Science, Artificial Intelligence, Data Science, Software Engineering, or a related field (Master's degree is a plus). Proven experience as a Generative AI Engineer, AI Engineer, Machine Learning Engineer, or a similar role. Strong programming skills in Python and experience building AI-powered applications. Hands-on experience with Large Language Models (OpenAI, Anthropic, Meta Llama, Google Gemini, or similar). Experience implementing Retrieval-Augmented Generation (RAG) systems. Knowledge of prompt engineering techniques and LLM optimization strategies. Experience with vector databases such as Pinecone, Weaviate, Chroma, Milvus, or FAISS. Familiarity with AI orchestration frameworks such as LangChain, LlamaIndex, or Semantic Kernel. Experience developing RESTful APIs and integrating AI services into production applications. Familiarity with cloud platforms such as AWS, Microsoft Azure, or Google Cloud Platform. Strong analytical, problem-solving, and communication skills. Preferred Qualifications Experience fine-tuning open-source LLMs using Hugging Face Transformers or similar frameworks. Knowledge of AI agents, autonomous workflows, and multi-agent systems. Experience with MLOps practices, model deployment, and monitoring. Familiarity with Docker, Kubernetes, and containerized AI deployments. Understanding of Natural Language Processing (NLP), embeddings, and semantic search. Experience with CI/CD pipelines and cloud-native AI architectures. Knowledge of Responsible AI, AI governance, and model evaluation frameworks. Technical Skills Programming Languages: Python, SQL, JavaScript (preferred) LLMs & AI Platforms: OpenAI, Anthropic Claude, Meta Llama, Google Gemini, Hugging Face AI Frameworks: LangChain, LlamaIndex, Semantic Kernel, Haystack Vector Databases: Pinecone, Weaviate, Chroma, Milvus, FAISS Cloud Platforms: AWS, Microsoft Azure, Google Cloud Platform Deployment & DevOps: Docker, Kubernetes, GitHub Actions, Jenkins Databases: PostgreSQL, MongoDB, Redis Version Control & Tools: Git, GitHub, Jupyter Notebook, VS Code, Postman What We Offer Competitive salary and comprehensive benefits package. Flexible work environment (onsite, hybrid, or remote). Opportunity to work on cutting-edge Generative AI and Large Language Model technologies. Access to modern AI infrastructure, cloud platforms, and development tools. Continuous learning through certifications, workshops, and industry conferences. Collaborative, innovative, and inclusive engineering culture. Clear career growth opportunities in Generative AI, Machine Learning, and AI Platform Engineering.

Required Skills ['AI', 'LLM Frameworks', 'langchain', 'Claude', 'Vector Databases (Pinecone Serverless', 'Qdrant', 'FAISS', 'ChromaDB)']

Additional Information

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