Principal AI Engineer

Trianz · Bangalore district, India

Company Overview

Trianz is an applied AI solutions company that accelerates customer business transformation through AI-powered \"Transformation Services as a Software Model.\" With 25+ years of transforming enterprises, we've evolved into a product-led, platform-driven organization serving global enterprises across Financial Services, Insurance, Healthcare, Hi-Tech, Manufacturing, and other industries.

With a global presence across 4 continents, our platform portfolio under the unified Concierto brand delivers end-to-end transformations including solutions for Migrate, Manage, Maximize, Modernize, Insights & Agentic AI, and SecOps , delivered through strategic partnerships with leading hyperscalers.

We're building the premier innovation-led organization in the digital transformation space through AI-first methodologies and data-driven excellence, RevolutionAIzing Transformations.

Role Overview

At Trianz, we are building sovereign, self-hostable AI products that enable enterprises to embrace AI while maintaining complete control over privacy, security, model access, and cost.

Our team is developing AI-native solutions for enterprise digital transformation, including a private conversational assistant for enterprise knowledge and productivity, as well as autonomous engineering agents capable of modernizing legacy enterprise applications.

We are looking for a Principal AI Engineer to define and drive the AI architecture powering these next-generation systems. This role spans AI research, evaluation frameworks, fine-tuning, inference optimization, enterprise-grade security, scalability, and production readiness.

This is a highly hands-on technical leadership role. You will be responsible for setting architectural direction, building production-grade AI systems, mentoring AI engineers, and staying at the forefront of emerging AI research and technologies.

Key Responsibilities

AI Architecture & Technical Leadership

• Define and drive the overall AI architecture for enterprise conversational assistants and autonomous engineering agents.
• Establish technical direction for AI system design, scalability, security, evaluation, and deployment.
• Lead architectural reviews and guide engineering best practices across AI initiatives.
• Mentor and guide junior AI engineers while maintaining a strong hands-on engineering presence.
• Drive research adoption and translate emerging AI innovations into production systems.

Advanced AI Systems & Agent Engineering

• Design and build sophisticated agentic AI systems capable of autonomous decision-making and workflow execution.
• Develop enterprise-scale conversational AI assistants with strong reasoning and task-execution capabilities.
• Build agent orchestration, planning, memory, tool usage, and complex multi-agent workflows.
• Architect and optimize agent evaluation frameworks for quality, reliability, and safety.
• Design AI solutions capable of handling complex, open-ended enterprise use cases.

Fine-Tuning, Evals & AI Optimization

• Define and implement large-scale evaluation frameworks for enterprise AI systems.
• Lead model fine-tuning initiatives using techniques such as instruction tuning, LoRA, QLoRA, DPO, RLHF, and related methodologies.
• Build and optimize LLM-as-a-judge evaluation systems and benchmarking frameworks.
• Drive model performance improvements through experimentation, testing, and continuous refinement.
• Research and implement advanced approaches for improving reliability, reasoning, and accuracy.

Self-Hosted Inference & Platform Engineering

• Design and optimize self-hosted inference architectures for enterprise deployment.
• Build scalable and cost-efficient AI infrastructure capable of supporting enterprise workloads.
• Optimize inference performance through quantization, caching strategies, model serving frameworks, and deployment enhancements.
• Work directly with inference stacks such as vLLM, Triton, and related technologies.
• Define deployment strategies supporting private, on-premises, and sovereign AI environments.

Research & Innovation

• Stay current with cutting-edge AI research, open-source projects, engineering blogs, and industry advancements.
• Evaluate and implement emerging techniques across reasoning, fine-tuning, evaluation, retrieval, and agent systems.
• Prototype and validate innovative AI solutions to drive product differentiation.
• Contribute to long-term AI strategy and technical roadmap.

Ideal Candidate Profile

Experience

• Master's or PhD in AI, Machine Learning, Data Science, Computer Science, or related field.
• 5+ years of hands-on experience as an AI or Machine Learning Engineer.
• 2+ years of experience as a Principal Engineer, Architect, Technical Lead, or Technical Engineering Manager for enterprise AI products.
• Proven experience architecting and deploying production-grade AI systems.
• Experience leading technical direction across complex AI initiatives.

AI & ML Depth

• Strong understanding of AI and Machine Learning fundamentals.
• Production experience in multiple areas such as:
• Agent Systems
• Evaluation Frameworks
• Fine-Tuning
• Self-Hosted Inference
• Enterprise AI Security
• Deep knowledge of LLM architecture, optimization, and deployment.
• Experience building sophisticated AI evaluation and benchmarking systems.
• Strong understanding of reasoning frameworks, agent architectures, and autonomous systems.

Advanced AI Expertise

• Experience with instruction tuning, LoRA, QLoRA, RLHF, DPO, or related fine-tuning approaches.
• Experience designing and implementing LLM evaluation frameworks.
• Exposure to advanced AI architectures and optimization techniques.
• Understanding of inference optimization techniques such as quantization, speculative decoding, and caching strategies.
• Familiarity with pretraining, continued pretraining, reward modeling, or preference optimization is highly desirable.

Platform & Infrastructure

• Experience deploying and operating AI solutions beyond managed cloud AI services.
• Hands-on experience with inference frameworks, model serving, GPU infrastructure, and production AI platforms.
• Strong understanding of distributed systems and scalable AI infrastructure.

Enterprise & Product

• Experience developing enterprise software products requiring security, privacy, compliance, and customization.
• Ability to translate enterprise business requirements into scalable AI solutions.
• Experience building AI products intended for large-scale enterprise environments.

Mindset & Fit

• Passionate about solving complex and ambiguous technical challenges.
• Comfortable operating in fast-moving startup-style environments.
• Strong ownership mindset with the ability to balance research and execution.
• Continuous learner with a deep interest in cutting-edge AI innovation.
• Hands-on technical leader who enjoys coding, experimentation, and mentoring.

Nice to Have

• Experience in cloud migration, infrastructure modernization, IT operations, or enterprise transformation domains.
• Familiarity with conversational AI platforms and engineering agents.
• Experience with autonomous AI systems that perform enterprise workflows.
• Contributions to open-source AI projects, research, or technical publications.
• Experience leveraging AI coding agents and AI-assisted software development workflows.