Responsibilities

• LLM Engineering & Model Development
• Architect, design, and implement LLM-driven systems for domain-specific technical challenges
• Develop and optimize prompt strategies for accuracy, consistency, and performance
• Fine-tune, benchmark, and deploy proprietary and open-source LLMs for enterprise workflows
• Design and build scalable, secure and self-hosted AI solutions
• Agent & Tooling Development
• Build AI-assisted tools that improve TME workflows, including document generation, content search, summarization, and knowledge access
• Develop autonomous and semi-autonomous agents capable of multi-step reasoning
• Design and deploy agents that plan tasks, use tools reliably, and adapt within defined constraints
• Integrate external APIs, internal systems, and custom utilities into agent architectures

Requirements

• Master’s degree in Computer Science, Machine Learning, Computer Engineering, Data Science, Software Engineering, or related field ( PhD preferred )
• Hands-on experience deploying, and adapting open-source LLMs (e.g. Llama, Qwen, Mistral) for on-prem solutions as well as integrating API based cloud services. Demonstrated through projects, coursework, internships, or open-source contributions.
• Proven experience designing and deploying LLM-based applications
• Strong command of advanced prompt engineering techniques (few-shot, chain-of-thought, system prompts)
• Solid understanding of tokenization, inference optimization, and context window management
• Familiarity with RAG systems, embeddings, and vector database
• Experience building or fine-tuning agents using frameworks such as Dataiku, TensorFlow, or PyTorch
• Familiarity with modern agent architectures (ReAct, tool use, memory systems, long-horizon planning)
• Experience with frameworks and libraries such as LangChain, LlamaIndex, or Hugging Face
• Understanding of GPU utilization and optimization for ML workloads
• Strong programming skills in Python (or similar), with production-quality coding practices
• Understanding of core software development practices (Git, testing, code reviews, documentation, and CI pipelines)
• Experience with Docker and cloud platforms (AWS, Azure, GCP)
• Ability to build robust ML/AI pipelines, data processing workflows, or automation scripts
• Ability to translate complex ML capabilities into polished, intuitive user experiences
• Interest in applied AI for engineering productivity and real‑world impact, rather than purely academic research
• Exposure to enterprise or infrastructure technologies (e.g., storage, networking, systems) is a plus

Preferred

• PhD preferred
• Experience with enterprise or infrastructure technologies (e.g., storage, networking, systems) is a plus