Artificial Intelligence Engineer

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:

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.

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.

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.

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.

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.

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.
• 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.

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.
• 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.