LLM Engineer

The Principal LLM Engineer will lead the design, fine-tuning, and optimization of large language model (LLM) systems that capture, contextualize, and deliver organizational expertise at scale. This role focuses on turning unstructured and tacit knowledge into intelligent, context-aware reasoning systems that support diagnostics, decision-making, and automation across the enterprise.

Key Responsibilities:

• LLM Architecture & Optimization
• Design and implement LLM-based reasoning frameworks for domain-specific knowledge activation and expert support.
• Architect Retrieval-Augmented Generation (RAG) pipelines integrating structured, semi-structured, and unstructured enterprise knowledge sources.
• Fine-tune and optimize foundation models for specific enterprise use cases (diagnostics, troubleshooting, process guidance, etc.).
• Develop prompt orchestration frameworks, including hierarchical prompts, context injection, and adaptive prompt tuning.
• Evaluate new LLM modalities (multimodal, reasoning-augmented, tool-using models) for continuous system improvement.

Knowledge Engineering & Representation:

• Collaborate with knowledge engineers to structure and embed expert content from documents, logs, and SME interviews.
• Develop semantic embedding strategies, vector stores, and hybrid retrieval methods combining symbolic and statistical reasoning.
• Create and maintain domain ontologies and entity graphs to ground model outputs in factual, validated enterprise data.
• Ensure model interpretability through citation, provenance tracking, and source-grounded responses.

Agentic & Multi-Model Integration:

• Integrate LLMs within multi-agent architectures, enabling autonomous reasoning and tool-assisted execution.
• Work with Agentic AI architects to design LLM–tool interaction schemas and context handoff mechanisms.
• Build adaptive memory systems combining vector, relational, and graph storage to support contextual continuity and learning loops.

Performance, Safety & Evaluation:

• Establish evaluation pipelines for accuracy, coherence, grounding, and bias mitigation.
• Implement reinforcement learning from human feedback (RLHF) or in-context learning feedback loops.
• Design A/B testing frameworks for comparing model prompts, architectures, and retrieval performance.
• Partner with governance and security teams to ensure compliance with data privacy and ethical AI standards.

Required Skills & Expertise:

• Core (LLM Engineering & Optimization)
• Deep expertise with transformer architectures, LLM fine-tuning, and instruction optimization.
• Strong experience with RAG systems, embedding models, and retrieval pipelines.
• Familiarity with multi-agent orchestration frameworks (AutoGen, Semantic Kernel, LangChain Agents).
• Proficiency in Python, PyTorch, and distributed inference optimization.
• Understanding of prompt engineering, context window management, and token efficiency strategies.

Knowledge Systems & Data Foundations:

• Experience building and maintaining knowledge graphs, vector databases, and context retrieval APIs.
• Ability to structure domain-specific taxonomies and embeddings for technical and industrial use cases.
• Familiarity with knowledge provenance, explainability, and citation generation mechanisms.

Evaluation & Experimentation:

• Expertise in designing model evaluation metrics, including factual accuracy, reasoning depth, and human preference modeling.
• Strong analytical background for evaluating LLM performance across complex domain-specific tasks.

Preferred Qualifications:

• 7+ years of experience in applied NLP, ML, or AI systems engineering.
• 2+ years hands-on with LLM fine-tuning, retrieval-augmented systems, or enterprise AI copilots.
• Graduate degree in Computer Science, Machine Learning, Computational Linguistics, or related field.
• Background in industrial, engineering, or diagnostics-related domains preferred.

Job Type: Full-time

Pay: ₹700, ₹1,400,000.00 per year

Benefits:

• Health insurance
• Life insurance

Application Question(s):

• Are you comfortable working in the EST time zone?

Experience:

• AI/ML : 5 years (Preferred)

Work Location: Remote