A company that’s revolutionizing AI-driven communications through phone, internet calls, and chat is looking for an experienced Machine Learning Engineer who can build, fine-tune, and optimize LLMs for client-specific use cases, integrating the latest AI frameworks and tools.

As they expand, they are focusing on custom Large Language Model (LLM) training tailored to client-specific domains and industry needs. They aim to push the boundaries of AI adaptability, performance, and usability for real-world applications.

What You’ll Do

• Train and fine-tune Large Language Models (LLMs) based on client domains and industry-specific data.
• Design, develop, and optimize custom AI workflows that integrate LLMs into production environments.
• Utilize LangChain, CrewAI, and LangFlow to orchestrate complex LLM-based applications.
• Implement and optimize retrieval-augmented generation (RAG) techniques for better contextual responses.
• Work on data preparation pipelines, including tokenization, augmentation, and embedding optimizations.
• Develop scalable and efficient inference pipelines for deploying LLMs in production.
• Collaborate with software engineers to integrate AI models into real-world applications.
• Optimize model performance, latency, and cost to ensure smooth deployment at scale.
• Research and experiment with cutting‑edge AI advancements in LLM fine‑tuning and prompt engineering.

What You’ll Bring

• 3+ years of experience in Machine Learning & NLP, with a focus on LLM training and deployment.
• Experience with LLM fine‑tuning techniques such as LoRA, PEFT, and instruction tuning.
• Proficiency in Python, PyTorch, TensorFlow, and Hugging Face Transformers.
• Hands‑on experience with LangChain, CrewAI, and LangFlow (bonus points for deep expertise).
• Strong understanding of vector databases (Pinecone, Weaviate, FAISS) and embedding models.
• Experience building production‑ready AI products, ensuring scalability and reliability.
• Deep knowledge of prompt engineering, tokenization strategies, and data augmentation for LLMs.
• Familiarity with ML‑Ops best practices, cloud‑based AI deployments, and GPU optimizations.
• A passion for AI‑driven automation, custom model development, and pushing the boundaries of LLM capabilities.

Bonus Points

• Experience deploying LLMs in low‑latency, real‑time environments.
• Strong background in serverless AI architectures and containerized deployments.
• Hands‑on experience with Kubernetes, Docker, and cloud-based ML workflows (AWS/GCP/Azure).
• Knowledge of speech‑to‑text (STT), text‑to‑speech (TTS), or conversational AI.

Company:

InspHire

Qualifications:

Language requirements:

Specific requirements:

Educational level:

Level of experience (years):

Senior (5+ years of experience)

Tagged as: Industry, Language Modeling, Machine Learning, NLP, Text-To-Speech, United Kingdom