Artificial Intelligence Engineer - Agentic AI

Description :

- Design and implement agentic AI pipelines using LangGraph, LangChain, CrewAI, or custom frameworks.

- Build robust retrieval-augmented generation (RAG) systems with vector databases.

- Fine-tune, evaluate, and deploy LLMs for task-specific applications.

- Integrate external tools and APIs into multi-agent workflows using dynamic tool/function calling (e.g., OpenAI JSON schema).

- Develop memory modules such as short-term context, episodic memory, and long term vector stores.

- Build scalable, cloud-native services using Python, Docker, and Terraform.

- Collaborate in agile, cross-functional teams to rapidly prototype and ship ML-based features.

- Monitor and evaluate agent performance using tailored metrics (e.g., success rate, hallucination rate).

- Ensure secure, reliable, and maintainable deployment of AI systems in production environments.

Your profile :

- 7+ years of professional experience in machine learning, NLP, or software engineering.

- Strong proficiency in Python and experience with ML libraries like PyTorch, TensorFlow, scikit-learn, and XGBoost.

- Hands-on experience with LLMs (e.g., GPT, Claude, LLaMA, Mistral) and NLP tooling such as LangChain, HuggingFace, and Transformers.

- Experience designing and implementing RAG pipelines with chunking, semantic search, and reranking.

- Familiarity with agent frameworks and orchestration techniques (e.g., planning, memory, role assignment).

- Deep understanding of prompt engineering, embeddings, and LLM architecture basics.

- Design systems with role-based communication, coordination loops, and hierarchical planning.

- Optimize agent collaboration strategies for real-world tasks.

- Solid foundation in microservice architectures, CI/CD, and infrastructure-as-code (e.g., Terraform).

- Experience integrating REST/GraphQL APIs into ML workflows.

- Strong collaboration and communication skills, with a builders mindset and willingness to explore new approaches.

Bonus Qualifications :

- Experience with RLHF, LoRA, or parameter-efficient LLM fine-tuning.

- Familiarity with CrewAI, AutoGen, Swarm, or other multi-agent libraries.

- Exposure to cognitive architectures like task trees, state machines, or episodic memory.

- Prompt debugging and LLM evaluation practices.

- Awareness of AI security risks (e.g., prompt injection, data exposure).