Solution Architecture & Deployment :

- Design and deploy secure, scalable GenAI architectures integrated into applications.

- Build and deploy REST APIs for AI/ML models.

- Work with Docker, Kubernetes in cloud environments (AWS/Azure/GCP).

GenAI & LLM Development :

- Fine-tune and optimize LLMs (GPT, VAEs, GANs, transformer-based models).

- Implement RAG pipelines, embedding, and prompt engineering techniques.

- Work with commercial and open-source LLMs (GPT, Claude, LLaMA, Phi).

Agentic AI Development :

- Build and deploy AI agents using LangChain, LangGraph, CrewAI, Autogen, AgentFlow.

- Implement multi-agent systems, orchestration, tool integration, and state management.

- Develop autonomous or semi-autonomous workflows for business use cases.

MLOps & Optimization :

- Set up end-to-end MLOps pipelines (CI/CD, monitoring, retraining).

- Optimize performance, scalability, and infrastructure costs.

- Use tools like Git, Docker, Kubernetes, vector databases.

Application Development & Data Integration :

- Develop APIs using FastAPI / Node.js.

- Work with React, TypeScript, async patterns, WebSockets/SSE.

- Handle data integration using REST APIs, SQL, and external systems.

Cross-Functional Collaboration :

- Partner with Engineering, Product, and Data teams.

- Communicate complex AI concepts clearly to technical and non-technical stakeholders.

- Stay updated with the latest advancements in GenAI and AI agents.

Required Skills :

- Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain).

- Hands-on experience with LLMs, RAG, embedding, and prompt tuning.

- Experience building AI agents and multi-agent systems.

- Experience with cloud platforms (AWS/Azure/GCP) and containerization.

- Strong knowledge of REST APIs and data integration.

- Experience with FastAPI, Node.js, React, TypeScript.

- Understanding of MLOps and deployment practices.

- Strong analytical, problem-solving, and communication skills.

Preferred :

- 4+ years of experience with GenAI/LLMs in production.

- Experience with agent orchestration frameworks (CrewAI, LangGraph, Autogen).

- Exposure to client-facing AI solutions or cross-functional projects.

- Open-source contributions, research, or AI project portfolio.

Requirements :

- Strong proficiency in Python, SQL, and GenAI frameworks (e.g., LangChain).

- Hands-on experience with LLMs, RAG, embedding, and prompt tuning.

- Experience building AI agents and multi-agent systems.

- Experience with cloud platforms (AWS/Azure/GCP) and containerization.

- Strong knowledge of REST APIs and data integration.

- Experience with FastAPI, Node.js, React, TypeScript.

- Understanding of MLOps and deployment practices.

- Strong analytical, problem-solving, and communication skills.

Benefits :

- Competitive salary and performance-based bonuses.

- Comprehensive insurance plans.

- Collaborative and supportive work environment.

- Chance to learn and grow with a talented team.

- A positive and fun work environment.