Key Responsibilities :

- GenAI Application Development: Design, develop, and deploy advanced GenAI agentic applications, leveraging leading LLMs (GPT, Mistral, LLaMA, Claude, Gemini) and specialized frameworks like LangGraph and Claude Code.

- Agentic Workflow Optimization: Build and refine agentic workflows with Prompt and Context engineering techniques.

- Model Training and Fine-Tuning: Utilize Reinforcement Learning with Verifiable Rewards (RLVR) to train and fine-tune domain-specific and code generation models.

- Quality Assurance and Safety: Rigorously evaluate GenAI outputs for common issues such as hallucinations, bias, and safety risks. Develop and implement robust guardrails to ensure reliability.

- Cross-functional Collaboration: Partner with engineering teams to integrate and operationalize agentic workflows, focused on boosting overall productivity and performance.

- Research and Optimization: Maintain current knowledge of the latest AI research and advancements to continuously optimize agentic workflows, improve model performance, enhance inference speed, and manage token costs effectively.

Required Qualifications & Skills :

- 6 to 10 years of total professional experience in software development, with a minimum of 2+ years specialized in Generative AI (GenAI) and Large Language Model (LLM) development.

- Strong proficiency in Python is mandatory.

- Mandatory hands-on expertise in Context Engineering and Advanced Prompt Engineering techniques.

- Proven experience with key machine learning/deep learning libraries such as Hugging Face Transformers, PyTorch, or TensorFlow.

- Deep knowledge and practical experience with Agentic and Coding Frameworks like LangGraph, AutoGen, CrewAI, Claude Code, and Gemini CLI.

- Solid understanding of Transformer-based LLM architectures and Embedding models.

- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, or a related technical field.

Core Competencies :

- Strong analytical and problem-solving skills to manage complex technical challenges.

- Excellent communication skills to translate complex AI concepts to non-technical stakeholders.

- Ability to work independently and mentor junior team members.