AI Engineer

Position:

Experience:

Location:

Company:

About Us:

Job Summary:

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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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.

1. 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:

• Bachelor’s/Master’s degree in CS, Engineering, Data Science, or related fields.
• 3–5 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.

If you are a dynamic and experienced AI Engineer looking to make a significant impact in the investment technology industry, we would love to hear from you!