- 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

- Bachelor’s/Master’s degree in CS, Engineering, Data Science, or related fields.

- 1-5 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.

Job Types: Full time, Permanent

Pay: Up to ₹2,500,000.00 per year

Work Location: In person

📌 AI Engineer (Bengaluru)
🏢 Growthsphereai Private
📍 Bengaluru