Artificial Intelligence Consultant

About the Company

Trianz believes that companies around the world face three challenges in their digital transformation journeys - shrinking time to transform due to competition & AI, lack of digital-ready talent, and uncertain economic conditions. To help clients leapfrog over these challenges, Trianz has built IP and platforms that have transformed the adoption of the cloud, data, analytics & insights AI. Specifically, the following Trianz platforms are changing the way companies approach transformations in various disciplines:

• Concierto: A fully automated platform to Migrate, Manage, and Maximize the multi & hybrid cloud. A zero code and SaaS platform, Concierto allows teams to migrate to AWS, Azure and GCP and manage them efficiently from a single pane of glass. Visit www.concierto.cloud for more information.
• Concierto Insights & Agentic AI: Built on the concept of federated or distributed data, Concierto Insights & Agentic AI revolutionizes how users access data anywhere in the companys ecosystems; productizes data and makes it available in a Netflix like user experience while delivering BI and AI powered insights.
• Pulse: Recognizing that workforces will be distributed, mobile, and fluid, Trianz has built a future of work digital workplace platform called Pulse. Visit www.trianz.com/Pulse.

About the Role

We are building our own enterprise-grade Large Language Model fine-tuned on domain-specific datasets to deliver high-accuracy, low-hallucination AI capabilities for enterprise customers. We are looking for an AI Engineering Leader who combines deep hands-on AI/ML expertise with the leadership ability to build and guide high-performing engineering teams. You will lead the end-to-end development of our LLM platform from fine-tuning pipelines and training infrastructure to inference optimization and enterprise deployment.

Responsibilities

• Lead a team of AI/ML engineers building the company's proprietary LLM and Generative AI platform.
• Define the technical roadmap for LLM development: pretraining strategies, fine-tuning pipelines, RLHF/RLAIF workflows, and alignment techniques.
• Oversee the design and implementation of scalable fine-tuning pipelines using enterprise-specific datasets.
• Drive research and engineering efforts to reduce hallucination, improve factual accuracy, and ensure reliable model outputs for enterprise use cases.
• Architect automated pipelines for data ingestion, model training, evaluation, and continuous improvement.
• Collaborate with AI Infrastructure engineers to optimize GPU utilization, distributed training, and inference serving.
• Establish model evaluation frameworks, benchmarks, and safety testing protocols.
• Partner with product and enterprise customer teams to understand requirements and translate them into AI capabilities.
• Mentor and grow a team of senior AI engineers; foster a culture of research-driven engineering.
• Stay current with state-of-the-art advancements in LLMs, GenAI, and enterprise AI deployment.

Qualifications

• 10+ years of software engineering experience with 5+ years focused on AI/ML systems.
• Strong hands-on background in Machine Learning deep learning architectures, transformer models, and NLP.
• Proven experience working with Large Language Models (GPT, LLaMA, Mistral, or equivalent).
• Experience with fine-tuning techniques: supervised fine-tuning (SFT), PEFT/LoRA, QLoRA, instruction tuning, RLHF.
• Proficiency in Python and ML frameworks: PyTorch, Hugging Face Transformers, DeepSpeed, or similar.
• Experience designing and managing distributed training pipelines on GPU clusters.
• Strong understanding of techniques to reduce LLM hallucination RAG, grounding, constrained generation, etc.
• Prior experience leading or managing AI engineering teams.
• Excellent communication skills able to translate complex AI concepts for non-technical stakeholders.

Preferred Skills

• Experience building enterprise AI products with domain-specific fine-tuned models.
• Familiarity with evaluation frameworks (RAGAS, LangSmith, Evals) and red-teaming / safety testing for LLMs.
• Exposure to multi-modal models or agentic AI orchestration frameworks.
• Publications or contributions to open-source AI/ML projects.