Artificial Intelligence Engineer - LangChain/LangGraph
First seen by Alion on Oct 22, 2026.
Job Summary :
We are seeking an experienced AI Engineer with hands-on expertise in building LLM-powered applications and agentic workflows using models such as GPT, Claude, Llama, and other frontier models. The ideal candidate will have experience fine-tuning LLMs, using LangChain/LangGraph for multi-step agents, implementing function-calling, developing robust Python applications, and building scalable systems on GCP. Experience in RAG architectures, data science, and production-grade AI systems is essential.
Key Responsibilities :
1. Develop and Deploy AI Applications :
- Design, develop, and deploy AI-driven applications utilizing large language models like GPT, Llama, and Claude.
- Implement and optimize algorithms for natural language processing (NLP) tasks and other AI-related functionalities.
- Create autonomous and semi-autonomous agents capable of planning, tool-use, and multi-step decision flows.
2. Utilize LangChain/LangGraph :
- Integrate LangChain and LangGraph to build robust agent workflows, tool orchestration, and memory-backed systems.
- Implement advanced capabilities like routing, evaluation loops, and agent monitoring.
3. Function Calling and API Integration :
- Develop and maintain function-calling mechanisms for seamless integration with other services and applications.
- Create and manage APIs to facilitate communication between AI models and external systems.
4. Python Development :
- Write clean, efficient, and scalable code in Python for AI and machine learning applications.
- Use modern Python libraries (Pydantic, async frameworks, orchestration tools) to build reliable systems.
5. GCP Cloud Services :
- Deploy and manage AI and machine learning applications on Google Cloud Platform (GCP) infrastructure.
- Utilize GCP services such as Compute Engine, Cloud Storage, Cloud Functions, and Vertex AI for model training, deployment, and storage.
6. Data Science and Machine Learning :
- Apply core ML and data science methods to improve model performance and system reliability.
- Build and maintain large-scale scraping and ingestion pipelines using Playwright, BeautifulSoup, etc.
7. Retrieval-Augmented Generation (RAG) Systems :
- Architect, optimize, and maintain RAG pipelines combining vector stores, embeddings, and hybrid search.
- Improve retrieval quality, latency, filtering, and contextual relevance.
8. Agentic Systems :
- Build multi-tool, multi-step agent workflows capable of autonomous reasoning, planning, and execution.
- Improve agent reliability through evaluation, guardrails, and structured output enforcement.
9. Collaboration and Communication :
- Collaborate with cross-functional teams to understand requirements and deliver AI solutions.
- Communicate technical concepts and project progress to stakeholders effectively.
Qualifications :
- Bachelors/Masters degree in CS, Engineering, Data Science, or related fields.
- 3 - 7 years of experience in AI/ML, with strong exposure to LLMs and agent frameworks.
- Strong Python skills; experience with LangChain, LangGraph, Pydantic, and async tooling.
- Hands-on GCP experience, including deploying production AI workloads.
- Understanding of ML fundamentals, embeddings, vector databases, and retrieval systems.
- Experience with function calling, API integrations, and RAG pipelines.
- Strong problem-solving skills and attention to detail.
- Ability to work independently and in collaborative environments.
Preferred Skills :
- Familiarity with multi-modal models and fine-tuning methods (LoRA, adapters).
- Knowledge of MLOps and model monitoring practices.
- Experience with containerization (Docker, Kubernetes).
- Strong documentation, experimentation, and evaluation skills.
- Experience with scraping frameworks like Playwright.
- Building or utilizing ontologies with AI systems.
- Experience with Databricks.
- Understanding of finance related concepts.
Skills
Artificial Intelligence, LLM, LangChain, LangGraph, NLP, GPT, Data Science, Python, Agentic AI, MLOps