AI Engineer

Overview

Key Responsibilities

• LLM/VLM Development & Integration: Design, train, fine-tune, and optimize LLMs and VLMs for real-world
• Agentic AI Systems: Develop and orchestrate autonomous agent frameworks capable of multi-step reasoning, planning, and tool use.
• Engineering & Deployment: Build scalable, low-latency inference systems for large models using frameworks like DeepSpeed, vLLM, TensorRT, or ONNX Runtime. Implement distributed training, model parallelism, and efficient inference pipelines; also optimize deployment for edge devices, GPUs, and cloud-based platforms.
• Research & Innovation: Stay up to date with the latest advancements in LLMs, multimodal models, and autonomous agents. Core Competencies
• AI/ML Expertise o Strong understanding of LLMs, VLMs, transformers, and multimodal architectures. o Experience with fine-tuning, LoRA/QLoRA, quantization, distillation, and evaluation. o Knoledge of neurosymbolic methodologi o Knowledge of reinforcement learning (RLHF, RLAIF) and alignment techniques.
• Agentic Frameworks o Experience with frameworks such as LangChain, LlamaIndex, AutoGPT, CrewAI, OpenAI Agents, Hugging Face Transformers/Agents. o Ability to design reasoning loops, memory systems, and multi-agent coordination.
• Development Tools & Libraries o Core AI frameworks: PyTorch, TensorFlow, Hugging Face, OpenAI APIs, DeepSpeed, vLLM. o Supporting tools: Weaviate, Pinecone, FAISS, Milvus (vector databases), Redis, Kafka. o Evaluation/monitoring: Weights & Biases, MLflow, TensorBoard, Evals frameworks.
• Programming Skills o Python – for AI research, prototyping, and deployment pipelines. o C++ – for performance-critical components, model inference optimization, and system integration.
• Systems & Infrastructure o Proficiency with Docker, and AI distributed training systems. o Strong knowledge of CUDA, GPU optimization, and high-performance computing. o Familiarity with cloud platforms (AWS, GCP, Azure) and edge deployment strategies.

Qualifications

• Master’s, or PhD in Computer Science, AI/ML, Robotics, or related field.
• Proven track record of hands-on work with LLMs, VLMs, or agentic frameworks.
• Experience in productionizing AI systems at scale.
• Excellent communication and collaboration skills.

Preferred (Nice-to-Have)

• Experience with reinforcement learning
• Background in robotics, simulation environments, or embodied AI.
• Publications in AI conference
• At TII, we help society to overcome its biggest hurdles through a rigorous approach to scientific discovery and inquiry, using state-of-the-art facilities and collaboration with leading international
• institutions.
• Our rigorous discovery and inquiry-based approach helps to forge new and disruptive breakthroughs in AI, advanced materials, autonomous robotics, cryptography, digital security, directed energy, quantum computing and secure systems.