LLM Engineer – AI Consulting & Strategy

Experience : 5+ Years Experience
Education : Bachelor’s/Master’s degree
Work Location : Chennai, India (Chennai/Remote/Hybrid)

Key Responsibilities :

• Design, build, and optimize RAG pipelines for enterprise AI applications.
• Develop automated prompt engineering frameworks using reinforcement and evaluation feedback loops.
• Fine-tune foundation models for domain-specific use cases, ensuring optimal accuracy and latency.
• Optimize token usage to reduce inference cost while maintaining semantic precision and relevance.
• Collaborate with AI Ops and MLOps teams to deploy optimized models into production environments.
• Continuously benchmark and improve model performance through data-driven evaluation methods.
• Maintain documentation of prompt, model, and dataset experiments for reproducibility and governance.

Required Technical Skills :

LLM Optimization & Performance

• Experience optimizing LLM inference performance across frameworks like vLLM, Hugging Face Transformers, or OpenAI APIs.
• Hands-on with quantization, pruning, distillation, and memory-efficient fine-tuning techniques (LoRA, QLoRA, PEFT).
• Knowledge of model evaluation frameworks (HELM, EvalHarness, DeepEval) for performance benchmarking.

RAG Architecture & Tuning

• Expertise in building Retrieval-Augmented Generation (RAG) pipelines using LangChain or LlamaIndex.
• Experience with vector databases such as Pinecone, Milvus, FAISS, or Weaviate.
• Ability to fine-tune retrieval embeddings, chunking strategies, and ranking optimization for contextual relevance.

Prompt Engineering & Automation

• Design, test, and optimize dynamic prompt templates for complex reasoning and domain-specific tasks.
• Develop automated prompt tuning pipelines using AI-assisted evaluation and reinforcement feedback.
• Implement prompt caching and adaptive prompt selection strategies to enhance inference performance.

Fine-Tuning & Adaptation

• Implement supervised fine-tuning (SFT), reinforcement learning from human feedback (RLHF), and domain adaptation methods.
• Use fine-tuning frameworks like Hugging Face Trainer, PEFT, or DeepSpeed.
• Manage model versioning, dataset curation, and experiment tracking using MLflow or Weights & Biases.

Token Usage & Cost Optimization

• Analyze and optimize token utilization through summarization, instruction compression, and dynamic truncation strategies.
• Implement intelligent token budgeting and API cost monitoring systems.
• Leverage AI-based analytics tools to optimize inference efficiency and output quality.

AI Infrastructure & Tooling

• Familiarity with cloud-based AI infrastructure (AWS, Azure, GCP, or OCI) for large-scale model operations.
• Experience with containerization, GPU orchestration, and deployment automation pipelines.
• Proficiency with Python, PyTorch, TensorFlow, and modern LLM SDKs.

Required Skills & Experience :

• 5+ years of experience in AI/ML or NLP engineering roles.
• Strong understanding of transformer architectures and attention mechanisms.
• Hands-on experience with LLM fine-tuning and inference optimization.
• Proficiency in building and deploying RAG systems in production environments.
• Experience with vector databases, embeddings, and retrieval frameworks.
• Proficient in Python and modern deep learning libraries such as PyTorch or TensorFlow.
• Familiarity with model evaluation, cost analysis, and performance profiling tools.