Agentic AI Engineer

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 to a product-led, platform-driven organization serving global enterprises across Financial Services, Insurance, Healthcare, Hi-Tech, Manufacturing, and other industries.

With 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

Trianz is taking its market-leading enterprise digital transformation SaaS platform and 20 years of experience in the digital transformation space and creating an AI-native, agentic digital transformation product. This isn’t bolting AI onto a legacy SaaS product - this is a complete reimagination. Think Harvey, but for digital transformation.

We’re looking for a founding-caliber AI engineer to help lead the design and build of this product.

This is a high-autonomy, high-impact role. You will be an early member of a new, small, fast-moving org, led by a former US seed-stage startup founder.

You’ll help shape the end-to-end architecture and work with cutting-edge AI technologies.

This is not a role for someone looking for stability or well-defined processes—it’s for someone who thrives when things are ambiguous and moving fast.

Key Responsibilities

Agentic AI System Development

• Define and implement the runtime AI architecture for a new AI-native application.
• Define and implement the development AI system - fine-tuning pipelines, evaluation frameworks, etc.
• Define and implement our AI-based SDLC.
• Design and build end-to-end agentic AI systems capable of automating enterprise workflows
• Develop systems incorporating orchestration, planning, tool usage, and memory management
• Implement reasoning-driven architectures (agent loops, multi-step execution, decision systems).

AI/ML Engineering

• Build and optimize knowledge bases and RAG pipelines for enterprise use cases
• Work on fine-tuning, evaluation, and deployment of models
• Contribute to model lifecycle management including monitoring, versioning, and continuous improvement

Platform & Architecture

• Define and evolve scalable AI system architectures for enterprise-grade applications
• Work on model deployment strategies, including deploying models without Bedrock, Vertex, and similar managed platforms

Product & Enterprise Integration

• Build solutions aligned with enterprise requirements such as security, compliance, and customization
• Collaborate with product teams to translate business problems into AI-driven solutions
• Contribute to building reusable platform capabilities within Concierto

Collaboration & Execution

• Work closely with cross-functional teams in a fast-paced, high-ownership environment
• Take end-to-end ownership of critical components from design to deployment
• Drive rapid iteration and experimentation

Ideal Candidate Profile

Experience

• 3+ years of software engineering experience
• 2+ years of hands-on experience building agentic AI systems
• Experience building end-to-end agentic systems—commercial products or mature internal tools—that have automated and replaced human processes. We’re looking for people who have built AI that does real work, not just AI tooling, copilots, or other supplementary features.
• Founding/early-stage engineer on an internal agentic venture within a larger company
• Ideally: founding engineer or early-stage senior engineer at an AI or other startup
• Background at vertical AI companies, AI-native startups, or mature Indian startups with serious AI implementation (e.g., Swiggy, Fresh works)

AI & ML Depth

• Strong understanding of AI system architecture: orchestration layers, tool use, memory, planning
• Experience with retrieval-augmented generation (RAG) pipelines
• Experience with fine-tuned models—training, evaluation, and deployment
• Experience with reasoning-focused approaches (chain-of-thought, tre…