AI Engineer

Role Overview

As an AI Engineer, you’ll drive the end-to-end development of advanced GenAI applications, including agentic AI agents with autonomous workflows, function calling, and LLM model fine-tuning. This role is for an experienced engineer who excels at building robust, production-grade AI systems and pushing the boundaries of what GenAI can accomplish.

Responsibilities

• Fine-tune and deploy open-source general purpose LLMs and code-generation models using PEFT, LoRA, QLoRA, instruction tuning, and RLHF.
• Design GenAI applications with agentic capabilities including autonomous, goal-driven agents.
• Implement function calling in workflows to enable models to invoke external tools, APIs, and databases.
• Build agentic RAG pipelines for dynamic data retrieval and reasoning.
• Create and maintain scalable pipelines including data ingestion, vector DB management, and MLOps frameworks like MLflow or Kubeflow.
• Containerize applications using Kubernetes and Docker.
• Optimize and monitor model performance, including latency, drift, bias, and security compliance.
• Collaborate cross-functionally to translate requirements into agentic GenAI solutions.

Requirements

• B.S or M.S. in Computer Science, Data Science, ML/AI or equivalent.
• 3-5 years building and deploying AI/ML solutions with demonstrable production experience.
• Extensive experience fine-tuning LLMs and code models with advanced techniques.
• Proven track record designing or delivering agentic GenAI applications.
• Proficiency in Python, PyTorch, TensorFlow, Hugging Face, LangChain, or LangGraph.
• Experience in Document and Image Analysis and extracting insights from unstructured data.
• Strong infrastructure skills in Docker, Kubernetes, CI/CD, and MLOps.

Skills

• Python
• PyTorch
• LangChain
• Kubernetes
• MLOps