Machine Learning Engineer - LLM Models (5-10 yrs)

Job Overview :

We are seeking a 4+ years of highly skilled LLM Engineer with expertise in working across various Large Language Model (LLM) engines to design, develop, and deploy cutting-edge AI-powered products.

The ideal candidate is passionate about LLMs, generative AI, and NLP and has hands-on experience in fine-tuning, optimizing, and integrating LLMs into scalable applications.

Key Responsibilities :

- LLM Model Selection & Optimization.

- Work with different LLM engines (GPT, LLaMA, Mistral, Claude, Gemini, etc.) to select the best fit for specific use cases.

- Fine-tune and optimize models for performance, efficiency, and cost-effectiveness.

- Implement prompt engineering, RAG (Retrieval-Augmented Generation), and memory-based techniques for enhanced outputs.

- AI Product Development.

- Design and develop LLM-powered applications, such as AI-driven assistants, chatbots, document automation, and search engines.

- Build scalable and innovative AI solutions that leverage multimodal models (text, voice, vision).

- Work on AI personalization strategies using embeddings, vector search, and advanced NLP pipelines.

- Integration & Deployment.

- Deploy LLMs using cloud platforms (AWS, Azure OpenAI, GCP Vertex AI) and MLOps frameworks (Hugging Face, LangChain, Ray).

- Implement model monitoring, logging, and bias detection mechanisms to ensure accuracy, fairness, and security.

- Work with APIs and microservices to integrate AI solutions into enterprise applications.

- Research & Innovation.

- Stay up to date with the latest advancements in LLMs, NLP, and generative AI.

- Experiment with new architectures and hybrid AI models to push the boundaries of AI innovation.

- Collaborate with cross-functional teams (Product, Engineering, Data Science) to drive AI-powered product enhancements.

Required Skills & Qualifications :

- Strong expertise in Python, TensorFlow, PyTorch, and JAX.

- Hands-on experience with LLM APIs & Open-Source Models (OpenAI, Hugging Face, Cohere, Mistral, etc.

- Deep understanding of transformer models, embeddings, tokenization, and fine-tuning techniques.

- Experience with RAG pipelines, vector databases (FAISS, Pinecone, Weaviate), and prompt tuning.

- Knowledge of cloud-based AI model deployment (AWS SageMaker, Azure ML, GCP Vertex AI).

- Familiarity with ethical AI, bias mitigation, and hallucination control in LLM applications.

Preferred Qualifications :

- Experience in building LLM-powered SaaS or enterprise products.

- Background in Reinforcement Learning (RLHF) and few-shot learning.

- Knowledge of multimodal AI (text, image, and voice models).

- Exposure to AI security, compliance, and governance frameworks.