AI Engineer

About the Role

We are looking for a highly skilled Agentic AI Engineer to design, build, and deploy intelligent AI agents capable of autonomous decision-making and task execution. The ideal candidate will have hands-on experience working with multiple Large Language Models (LLMs), agent frameworks, vector databases, and cloud platforms, along with strong DevOps and production deployment awareness.

You will be responsible for building AI-driven systems using agentic architectures, integrating LLMs with tools, APIs, and data sources to create scalable and reliable enterprise AI applications.

Key Responsibilities

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Design and implement agentic AI architectures for autonomous task execution and decision-making.

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Develop and deploy LLM-powered applications and AI agents for real-world business use cases.

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Integrate multiple LLM providers (OpenAI, Anthropic, open-source models, etc.) into AI systems.

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Build and manage Retrieval-Augmented Generation (RAG) pipelines using vector databases.

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Develop solutions using AI frameworks such as Lang Chain, LlamaIndex, Autogen, Crew AI, or similar.

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Work with multiple vector databases for semantic search and memory management.

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Design multi-agent systems that collaborate to solve complex tasks.

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Deploy AI solutions across multi-cloud environments (AWS, Azure, GCP).

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Implement CI/CD pipelines, containerization, and infrastructure automation for AI workloads.

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Monitor and optimize AI model performance, cost, latency, and reliability.

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Collaborate with data engineers, ML engineers, and product teams to deliver AI solutions.

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Ensure security, governance, and responsible AI practices.

Required Skills

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Strong experience with Python and AI/ML development.

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Hands-on experience with multiple LLMs (OpenAI, Anthropic, Cohere, Mistral, Llama, etc.).

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Experience building agent-based AI systems.

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Experience with AI orchestration frameworks (LangChain, LlamaIndex, CrewAI, AutoGen, or similar).

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Hands-on experience with vector databases such as Pinecone, Weaviate, Milvus, Chroma, or FAISS.

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Experience implementing RAG architecture.