AI Engineer (Agentic AI & LLM Systems)

Key Responsibilities

• 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
  

• Develop memory modules such as short-term context, episodic memory, and long
  

• Build scalable, cloud-native services using Python, Docker, and Terraform
• Collaborate in agile, cross-functional teams to rapidly prototype and ship ML-based
  

• Monitor and evaluate agent performance using tailored metrics (e.g., success rate,
  

• Ensure secure, reliable, and maintainable deployment of AI systems in production
  

• 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 builder’s 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)