- Design, architect, and implement state-of-the-art Generative AI applications and agentic systems using modern AI frameworks such as LangChain, LlamaIndex, or custom orchestration layers.

- Seamlessly integrate large language models (i.e., GPT-4, Claude, Mistral) into production workflows, tools, or customer-facing applications.

- Build scalable, reliable, and high-performance backend systems using Python and modern frameworks to power GenAI-driven features.

- Take ownership of prompt engineering, tool usage, and long/short-term memory management to develop intelligent and context-aware agents.

- Deliver high-quality results rapidly by leveraging Cursor and other AI-assisted "vibe coding" environments for fast development, iteration, and debugging.

- Use vibe coding tools effectively to accelerate delivery, reduce development friction, and fix bugs quickly with minimal overhead.

- Participate in and lead the entire SDLC, from requirements analysis and architectural design to development, testing, deployment, and maintenance.

- Write clean, modular, well-tested, and maintainable code, following best practices including SOLID principles and proper documentation standards.

- Proactively identify and resolve system-level issues, performance bottlenecks, and edge cases through deep debugging and optimization.

- Collaborate closely with cross-functional teams-including product, design, QA, and ML-to iterate quickly and deliver production-ready features.

- Execute end-to-end implementations and POCs of agentic AI frameworks to validate new ideas, de-risk features, and guide strategic product development.

- Contribute to internal tools, libraries, or workflows that enhance development speed, reliability, and team productivity.

What You Bring :

- Extensive Python Expertise : Hands-on experience in Python development, with a focus on clean, maintainable, and scalable code.

- Software Design Principles : Mastery of OOP, SOLID principles, and design patterns; proven experience designing and leading complex software architectures.

- GenAI Frameworks : Practical experience with frameworks like LangChain, LlamaIndex, or other agent orchestration libraries.

- LLM Integration : Direct experience integrating APIs from OpenAI, Anthropic, Cohere, or similar providers.

- Prompt Engineering : Strong understanding of prompt design, refinement, and optimization for LLM-based applications.

- RAG Systems : Experience architecting and implementing Retrieval Augmented Generation (RAG) pipelines and solutions.

- Vector Databases : Exposure to FAISS, Pinecone, Weaviate, or similar tools for semantic retrieval.

- NLP/ML Knowledge : Solid foundation in Natural Language Processing and core machine learning concepts

- Cloud & Deployment : Familiarity with cloud platforms like AWS, GCP, or Azure, including deployment of GenAI solutions at scale.

- Containerization & Orchestration : Proficient with Docker, with working knowledge of Kubernetes.

- MLOps / LLMOps Tools : Experience with platforms such as MLflow, Weights & Biases, or equivalent tools.

- Semantic Search / Knowledge Graphs : Exposure to knowledge graphs, ontologies, and semantic search technologies.

- Development Lifecycle : Strong grasp of SDLC processes, Git-based version control, CI/CD pipelines, and Agile methodologies