Artificial Intelligence Engineer

Artificial Intelligence Engineer

You will collaborate with cross-functional teams to develop scalable AI applications, autonomous agents, and knowledge systems that integrate with real-world products.

Key Responsibilities

• Design, build, and maintain

  LLM-powered applications

  , including chatbots, autonomous agents, and decision-support tools.
• Develop and optimize

  RAG pipelines

  using vector databases, embeddings, metadata filtering, and document preprocessing.
• Implement workflow orchestration using

  LangGraph

  , including event-driven agents, state management, and multi-step AI reasoning.
• Build AI solutions using

  LangChain

  components such as chains, tools, agents, memory, and custom integrations.
• Develop

  Agentic AI systems

  capable of multi-step task execution, planning, and reasoning.
• Write clean, scalable

  Python

  code for model serving, data processing, and API integrations.
• Fine-tune models, evaluate LLM outputs, and ensure reliability, grounding, and safety.
  
• Integrate AI systems with existing backend services, cloud platforms, and product architectures.
  
• Collaborate with ML engineers, data engineers, and product teams to deliver production-quality solutions.
  
• Monitor, evaluate, and optimize model performance, latency, and retrieval quality.
  

Required Skills & Qualifications

• Strong proficiency in

  Python

  and modern Python frameworks.
• Practical experience building RAG systems using tools like:
  

• FAISS

  ,

  Chroma

  ,

  Pinecone

  ,

  Weaviate

  , or similar vector DBs.
• Hands-on experience with

  LangChain

  (chains, agents, tools, retrieval, memory).
• Strong understanding of

  LangGraph

  workflows and agent orchestration.
• Deep understanding of

  LLMs

  , embeddings, tokenization, prompt engineering, and evaluation.
• Experience building

  Agentic AI

  architectures (planning, reasoning, multi-agent workflows).
• Familiarity with LLM APIs (OpenAI, Anthropic, Groq, HuggingFace, etc.).
  
• Knowledge of REST APIs, microservices, and backend integration.
  
• Experience with cloud platforms (AWS, GCP, Azure) and containerization (Docker).
  
• Strong debugging, problem-solving, and algorithmic thinking skills.
  

Preferred Qualifications (Nice-to-Have)

• Experience with fine-tuning or supervised instruction tuning.
  
• Background in NLP, machine learning, or knowledge graphs.
  
• Familiarity with data engineering pipelines.
  
• Experience with event-driven architectures or streaming systems.
  
• Contributions to open-source LLM frameworks (LangChain, LangGraph, etc.).
  

Soft Skills

• Strong communication and cross-team collaboration.
  
• Ability to translate business requirements into AI solutions.
  
• Highly self-driven, curious, and comfortable working with emerging technologies.