Generative AI

Job Description

AI Engineer JD (2+ years) Job Description Were searching for a hands-on AI engineer who can take modern LLMs from prototype to production, orchestrating multi-agent workflows using libraries such as LlamaIndex Workflows, LangGraph , and structured function calling . You should bring a solid foundation in classical ML (e.g., XGBoost) and deep learning with transformer-based models especially LLaMA and Qwen-family models along with experience in Retrieval-Augmented Generation (RAG) pipelines. A track record of building reliable, scalable systems is essential. Responsibilities: Design, build, and deploy ML/DL models for vision (YOLO), tabular (XGBoost),, and NLP / GenAI use-cases (function calling, RAG). Work on fine-tuning and deploying LLMs using platforms like Hugging Face and PyTorch. Engineer agent-based LLM solutions Compose multi-step, tool-calling workflows with LlamaIndex or LangGraph. Implement structured function calling and dynamic tool selection for Retrieval-Augmented Generation (RAG) pipelines. Orchestrate agent state, memory, and conversation context to solve complex user tasks. Fine-tune and serve LLMs on Hugging Face / PyTorch, including efficient-tuning methods (LoRA, QLoRA, PEFT). Operate on cloud (Azure preferred, AWS acceptable) set up training jobs, GPU/ACI deployments, CI/CD, observability, and cost governance. Collaborate cross-functionally with product, data, and frontend teams to turn fuzzy ideas into measurable impact. Build FastAPI micro-services for low-latency inference, streaming responses, and secure integration with downstream systems. Requirements: Proficiency in Python, with exposure to FastAPI and/or Java. Solid understanding and practical experience in Machine Learning with models like XGBoost. Experience with Deep Learning using YOLO, OCR frameworks, and PyTorch. NLP / GenAI: LLM fine-tuning, prompt engineering, RAG design. Hugging Face Transformers, PEFT, vector databases. Experience implementing agent frameworks (LlamaIndex, LangGraph, LangChain Agents) and function-calling patterns. MLOps: Docker, CI/CD, experiment tracking, model versioning, monitoring, and rollback strategies. Cloud: Azure ML / Azure Functions / AKS (preferred) or AWS SageMaker / Lambda basics. Bonus: experience with Triton / vLLM, streaming websockets, or GPU cost-optimization. Benefits of Working with Us: Best of Both Worlds: Enjoy the enthusiasm and learning curve of a startup combined with the deliveries and performance of an enterprise service provider. Flexible Working Hours: We offer a delivery-oriented approach with flexible working hours to help you maintain a healthy work-life balance. Limitless Growth Opportunities: The sky is not the limit when it comes to learning, growth, and sharing ideas. We encourage continuous learning and personal development. Flat Organizational Structure: We don't follow the typical corporate hierarchy ladder, fostering an open and collaborative work environment where everyone's voice is heard. As part of our dedication to an inclusive and diverse workforce, TechChefz Digital is committed to Equal Employment Opportunity without regard to race, color, national origin, ethnicity, gender, protected veteran status, disability, sexual orientation, gender identity, or religion. If you need assistance, you may contact us at joinus@techchefz.com