Director, Product Development

mrisoftware; Bengaluru; On-site; INR 4,000,000 - 7,000,000; Full time

Job summary

MRI Software seeks a Director of AI Engineering to lead architecture and delivery of AI capabilities across MRI's platform and product portfolio. This technical leadership role owns how AI is built, integrated, and operated at scale, and shapes the engineering organization behind it.

The Director will manage the shared AI platform, data foundation, and AI infrastructure economics, champion modern AI practices, and collaborate with executives on AI trust, adoption, and business impact.

Qualifications

• 10+ years of engineering leadership experience, including hiring, org design, and scaling multi-team organizations.
• Deep hands-on experience architecting and delivering AI/ML systems, including LLM-based apps, RAG architectures, and agentic AI patterns.
• Advanced Python skills with ability to engage in architecture reviews and hands-on problem solving.
• Experience with Model Context Protocol or comparable interoperability frameworks for AI systems.
• Strong background in API design, microservices, and cloud-native architectures (Azure, AWS, GCP).
• Experience improving engineering delivery velocity through tooling, processes, and AI-assisted development practices.
• Familiarity with product lifecycle management and collaborating with product management without owning the roadmap.
• Strong understanding of data pipelines, data platforms, and AI data foundations.

Responsibilities

• Define and drive MRI's AI engineering roadmap, including platform architecture and technology direction.
• Lead and scale the AI engineering organization responsible for model integration, RAG systems, agent orchestration, and AI platform services.
• Own the shared AI platform, including prompt management, evaluation frameworks, vector databases, and model routing services.
• Champion adoption of Model Context Protocol and other interoperability standards across MRI's product ecosystem.
• Define enterprise agent ecosystem approach, including interoperability, context management, cross-product orchestration, and reusable agents.

Job description

Director, AI Engineering

MRI Software is seeking a Director of AI Engineering to lead the architecture and delivery of AI capabilities across MRI's platform and product portfolio. This is a technical leadership role, owning how AI gets built, integrated, and operated at scale, and building the engineering organization behind it.

The Director will own the shared AI platform and its underlying data and knowledge foundation, drive AIOps practices and economic accountability for AI infrastructure, and champion modern AI engineering practices including agentic systems, Model Context Protocol and related interoperability standards, AI assisted development, and enterprise agent ecosystem design. The role establishes reusable architecture patterns and self‑service platform capabilities across MRI's business units, partners closely with product, IT, data, and security leadership, and drives adoption of AI‑native engineering practices within the organization. The Director also engages directly with customers and executive leadership on AI trust, adoption, and business impact.

Key Responsibilities

• Define and drive MRI's AI engineering roadmap, including build versus buy decisions, platform architecture, and technology direction.
• Lead and scale the AI engineering organization responsible for model integration, RAG systems, agent orchestration, and AI platform services, including hiring, org design, and coaching engineering leaders and architects.
• Own the shared AI platform, including prompt management, evaluation frameworks, vector databases, model gateway and routing services, and agent registries.
• Champion adoption of Model Context Protocol and other emerging interoperability standards to connect AI capabilities across MRI's product ecosystem.
• Define MRI's enterprise agent ecosystem approach, including agent interoperability, shared memory and context management, cross-product orchestration, and reusable agent capabilities across the portfolio.
• Architect and scale agentic AI ecosystems that operate across multiple products, data sources, and internal systems.
• Define AI reference architectures and reusable patterns for the AI engineering organization, driving standardization over product level fragmentation.
• Partner with product leadership to translate AI roadmap priorities into engineering execution and sequencing.
• Establish engineering practices, tooling, and governance that increase delivery velocity for AI powered features across the organization.
• Drive AI assisted development practices, including AI assisted coding and workflow tooling, to improve engineering team productivity.
• Own model lifecycle management, from evaluation and selection through deployment, monitoring, and retirement.
• Select the best fit models and AI technologies for a given use case, balancing cost, quality, and scale tradeoffs.
• Establish AIOps and LLMOps practices, including observability and telemetry, hallucination detection, and continuous evaluation and benchmarking of models and agents.
• Own AI infrastructure and model consumption costs, establishing unit economics and ROI measurement for AI features and driving FinOps discipline across the AI platform.
• Set standards for API design and integration patterns that support AI feature delivery across MRI's platforms.
• Own the knowledge architecture underlying AI systems, including semantic layers, metadata and context management, data governance for AI, and knowledge graph design.
• Partner with data engineering and platform teams to ensure the data foundation supports AI initiatives at scale.
• Evaluate and pilot emerging AI technologies, translating industry trends into practical initiatives for MRI.
• Establish governance and responsible AI practices for model usage, data handling, and agent behavior, including AI and agent security, threat modeling, prompt injection defense, data leakage prevention, and identity and authorization patterns for agents.

Qualifications

• 10 or more years of engineering leadership experience, including hiring, org design, and developing engineering talent while scaling multi team organizations.
• Deep hands on experience architecting and delivering AI and ML systems, including LLM based applications, RAG architectures, and agentic AI patterns.
• Advanced Python skills, with the ability to engage directly in architecture review and hands on technical problem solving.
• Experience with Model Context Protocol or comparable interoperability and integration frameworks for AI systems.
• Strong background in API design, microservices, and modern cloud native architectures such as Azure, AWS, or GCP.
• Experience improving engineering delivery velocity through tooling, process, and AI assisted development practices.
• Working familiarity with product lifecycle management practices and the ability to partner effectively with product management without owning the product roadmap directly.
• Strong understanding of data pipelines, data platforms, and the data foundations required for AI at sc