Title:  Technical Specialist-App Development

Area(s) of responsibility

Agentic AI Technical Developer (Must have  - Autogen/CrewAI/LangGraph)

This is not a data science role. This is completely implementation centric role in core AgenticAI application engineering projects  The Implementation Technical Developer will be responsible for designing, developing, and deploying cutting-edge Agentic AI solutions using the latest Large Language Models (LLMs) and frameworks. This role requires deep expertise in Python programming, cloud platforms (Azure, GCP, AWS), and advanced AI techniques such as Agent Patterns, Agent to Agent, MCP, fine-tuning, LLMOps, and Responsible AI. will lead to the development of scalable, secure, and efficient Agentic AI applications, ensuring alignment with business goals and technical requirements.

Key Responsibilities:

1. Design and Develop: Create scalable and modular solution for AgenticAI applications using frameworks like Autogen, Crew.ai, LangGraph, LlamaIndex, and LangChain.
2. Python Development: Core hands on experience development of Python-based AgenticAI applications, ensuring high-quality, maintainable, and efficient code.
3. Data Curation Automation: Build tools and pipelines for automated data curation, preprocessing, and augmentation to support LLM training and fine-tuning.
4. Cloud Integration: Design and implement solutions leveraging Azure, GCP, and AWS LLM ecosystems, ensuring seamless integration with existing cloud infrastructure.
5. Fine-Tuning Expertise: Apply advanced fine-tuning techniques such as PEFT, QLoRA, and LoRA to optimize LLM performance for specific use cases.
6. LLMOps Implementation: Establish and manage LLMOps pipelines for continuous integration, deployment, and monitoring of LLM-based applications.
7. Responsible AI: Ensure ethical AI practices by implementing Responsible AI principles, including fairness, transparency, and accountability.
8. RLHF and RAG: Implement Reinforcement Learning with Human Feedback (RLHF) and Retrieval-Augmented Generation (RAG) techniques to enhance model performance.
9. Modular/Agentic RAG Design: Develop and optimize Modular RAG/Agentic architectures for complex Agentic applications.
10. Open-Source Collaboration: Leverage Hugging Face and other open-source platforms for model development, fine-tuning, and deployment.
11. Front-End Integration: Collaborate with front-end developers to integrate Agentic capabilities into user-friendly interfaces.
12. SDLC and DevSecOps: Implement secure software development lifecycle (SDLC) and DevSecOps practices tailored to LLM-based projects.
13. Technical Documentation: Create detailed design artifacts, technical specifications, and architecture diagrams for complex projects.
14. Stakeholder Collaboration: Work closely with cross-functional teams, including data scientists, engineers, and product managers, to deliver high-impact solutions.
15. Mentorship and Leadership: Guide and mentor junior developers and engineers, fostering a culture of innovation and technical excellence.