Senior AI Engineer - Director
vialto ·
- Seniority
- Senior
- Employment
- Full time
- Category
- AI engineering
vialto ·
Vialto Partners · Bengaluru, Karnataka, India
Vialto Partners · Bengaluru, Karnataka, India
M&G · Edinburgh
Virtuos · China
Hands-on Director-level AI Engineer leading the design, building, and scaling of production-grade GenAI and agentic AI solutions using Python, FastAPI, and Azure to transform tax and immigration service delivery at Vialto Labs.
About Vialto Labs (VLabs)
Vialto Labs (VLabs) is responsible for redesigning how work is delivered in the tax and immigration service lines, as well as driving operational efficiency across Vialto’s functional areas using AI. The team builds and deploys novel AI-enabled solutions that directly improve productivity and increase delivery quality for our clients. VLabs is accountable for rapidly turning innovative experiments into production-ready deliverables at scale and embedding them into day-to-day operations. This team focuses on the highest-impact workflows, creating standardized, repeatable capabilities that can be deployed globally. Operating with a mandate for speed and measurable outcomes, VLabs works alongside service line, product, and platform leaders.
About the Role
The Director, Senior AI Engineer is responsible for designing, building, and scaling AI-powered delivery capabilities across Vialto. This is a hands-on role that combines deep technical execution with the responsibility to help a team of engineers achieve their goals.
Operating at the intersection of AI engineering, delivery transformation, and innovation, this role owns execution from concept through production. The role will work directly with the Global Transformation leader and collaborate with Product, Platform, and business leaders to embed AI into core operations, transforming how work is delivered across tax, immigration, and enterprise functions.
This role requires a leader who can personally engage in solution design, architecture, and hands-on engineering decisions, while also building a scalable, high-performing team of AI engineers and specialists. Success is defined by the ability to move rapidly from idea /experiment to production, standardize solutions, and deliver measurable improvements in productivity and quality across the business.
As a technical builder with a strong business acumen, this role will drive the adoption of AI across the organization, aligning stakeholders, reshaping workflows, and ensuring accountability for outcomes. The role spans both service delivery transformation and internal enterprise functions (e.g., Finance, HR), requiring a balance of technical depth, operational rigor, and commercial awareness.
Technically, this role demands strong hands-on experience building production-grade GenAI and agentic AI solutions, with a focus on Python-based backend engineering, API development (e.g., FastAPI), and cloud-native architectures on Azure. The leader will guide the design and deployment of agent-based systems, orchestration frameworks (e.g., Microsoft Agent Framework or similar), evaluation of pipelines, and secure, scalable AI applications integrated into real business workflows.
Key Responsibilities
AI Engineering Leadership
Drive the engineering strategy and execution for AI-enabled delivery solutions across the VLabs portfolio
Build and deploy production-grade AI capabilities that improve delivery efficiency, quality, speed, and scalability
Define reference patterns for AI applications, including orchestration, retrieval, workflow automation, evaluation, monitoring, and cost control
Make sound decisions on architecture, tooling, build-vs-buy, and model usage in alignment with enterprise standards and business goals
Technical Delivery
Design, build, and maintain backend services for GenAI and agent-based applications using Python
Develop APIs and services using FastAPI
Build and support AI solutions using the Microsoft Agent Framework (or others) and Azure cloud services
Design and integrate agent workflows, tool usage, orchestration, and workflow management patterns
Build and maintain services using PostgreSQL, Cosmos DB, and/or SQL Server
Containerize and deploy applications using Docker and Kubernetes
Develop secure, scalable, and production-ready AI systems on Azure
Build and maintain agentic harnesses, test frameworks, and evaluation pipelines
Implement observability, monitoring, logging, and quality checks for AI systems
Use modern engineering tools and workflows including Azure DevOps (ADO), GitHub, Claude Code, Cowork, and automation-friendly development practices
Ensure adherence to Responsible AI, security, privacy, and governance requirements
Support document intelligence, OCR, and data processing workflows where needed
Team and Stakeholder Leadership
Effectively collaborate with and support the Global Transformation leader to deliver measurable value for Vialto Partners
Create the conditions for AI engineers and technical specialists to deliver at speed, partnering closely with AI Architecture, Governance & Security, and AI Ops & Support team members. Work closely with senior leaders across Product, and Technology to align priorities and remove blockers
Foster a culture of high standards, speed, accountability, and practical innovation
Qualifications & Experience
The ideal candidate is a builder, operator, and transformation catalyst. They bring deep technical expertise in AI engineering, Python-based backend development, and cloud-native systems, combined with a proven ability to build teams and deliver enterprise-scale impact.
They are equally comfortable writing or reviewing Python code, defining system architecture, and leading executive-level conversations about transformation and value creation. They have hands-on experience developing GenAI, LLM, and agent-based applications, including orchestration, evaluation frameworks, and integrating APIs, tools, and workflows into production environments.
This individual thrives in fast-moving environments and brings structure, discipline, and engineering rigor to GenAI initiatives. They focus on delivering production-grade solutions, not prototypes, and have a track record of scaling AI capabilities that drive measurable business outcomes.
They are well-versed in influencing senior stakeholders, driving adoption across business units, and translating AI investments into tangible productivity gains and business outcomes. They build strong, accountable teams and create a culture of speed, ownership, and practical innovation.
Experience & Leadership
15+ years of software engineering experience or equivalent depth demonstrated through accelerated career impact
Proven experience leading engineering teams in enterprise or production AI environments
Track record delivering large-scale transformation initiatives with quantifiable business impact
Experience operating in global delivery or shared services environments preferred
Strong ability to connect technology investments to P&L outcomes and operational performance
AI & Engineering Expertise
Hands-on experience building and deploying GenAI / LLM / agent-based applications in production
Strong backend engineering expertise (Python, APIs, distributed systems)
Experience with agent orchestration frameworks (e.g., Microsoft Agent Framework or similar)
Proven experience designing evaluation frameworks, monitoring systems, and AI quality controls
Deep understanding of secure, scalable AI system design (data handling, access control, reliability)
Technology Stack
Cloud: Azure (preferred), cloud-native architecture
Backend: Python, FastAPI (or similar)
Data: Postgres, Cosmos DB, SQL Server (or equivalent)
Infrastructure: Docker, Kubernetes
DevOps: GitHub, Azure DevOps (ADO), CI/CD pipelines
AI Capabilities:
Agentic systems and multi-agent workflows
RAG, retrieval pipelines, and knowledge integration
Workflow orchestration and automation
OCR, document intelligence, and unstructured data processing
Tool integration, APIs, and structured workflows
MCPs, SKILLs, plugins, tool calling or reusable agent capabilities
Operating & Business Capabilities
Ability to align executive stakeholders and translate strategy into execution
Financial and commercial acumen
Experience embedding AI into real business processes, not just prototypes
Education
Bachelor’s degree required
Advanced degree (MBA, MS in AI/Data/Engineering) preferred
Additional Information:
Allianz · Munich