Job Description :

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.

- 2-5 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.