AI Architect

ABOUT SONATA SOFTWARE

Sonata Software, with over $1.2 Billion Revenue, is a leading AI-first Modernization Engineering company, powered by our unique PlatformationTM framework. Our 6400+ AI Engineers are helping enterprises transform from systems to intelligent business platforms leveraging partner ecosystem to drive speed, efficiency and growth. Our bouquet of Modernization Engineering Services with AI -first approach cuts across Cloud, Data, AI, Dynamics, Test Automation, and Managed Services.

Sonata’s AI-led modernization is enabled by a powerful suite of proprietary tools and assets. At the core is the Harmoni.AI Enterprise Platform, which includes Agent Bridge-a governance and observability framework; Agent Builder-a development toolkit for fungible agents integrated into enterprise systems; and the Agent Marketplace-an internal ecosystem for modular, reusable agents.

Headquartered in Bengaluru, India, Sonata Software has a strong global presence, including key regions US, UK, India, Malaysia, Mexico, Australia, DACH (Germany/Switzerland) & Nordics (Finland). We are a trusted partner of Fortune 500 companies in Banking, Financial Services and Insurance; Healthcare and Lifesciences; Telecom, Media, and Technology; and Retail, Manufacturing and Distribution space.

Deep collaborations with partners like Microsoft, AWS, Salesforce, and Snowflake strengthen our ability to deliver cutting-edge AI solutions. Our 30+years of partnership with Microsoft, and being part of the elite Microsoft Inner Circle, places us among the top 1% of global Microsoft partners. Sonata is now among the first companies to earn the Microsoft Frontier Partner Badge Also; Sonata Software is proud to achieve AWS Premier Tier Status in the AWS Partner Network.

Title: AI Architect

Location: New Jersey/ Seattle/San Francisco

Experience: 12+ years overall

AI / ARCHITECTURE: 5+ years AI/ML; 3+ years architecture

Role Overview

We are seeking an experienced AI Architect to define, shape, and drive enterprise-scale Artificial Intelligence, Machine Learning, Generative AI, and Agentic AI solutions. The role combines deep architecture expertise with customer-facing presales leadership, translating business priorities into differentiated solution strategies, compelling proposals, and scalable production architectures. The AI Architect works with business leaders, enterprise architects, engineering, data, security, sales, alliance partners, and customers across the full lifecycle, from discovery and qualification through PoC, proposal, implementation, and scale.

Key Responsibilities

AI, GenAI & Agentic Architecture

• Define enterprise AI and Agentic AI reference architectures, technology standards, and reusable solution patterns.
• Architect solutions using LLMs, multimodal models, RAG, AI agents, machine learning, NLP, and intelligent automation.
• Design RAG architectures covering ingestion, chunking, embeddings, vector search, retrieval, reranking, prompt orchestration, citations, and grounded response generation.
• Design agentic patterns for tool use, workflow orchestration, multi-agent collaboration, memory, and human-in-the-loop controls using Microsoft Copilot and Azure AI, Anthropic, or AWS services.
• Evaluate build-vs-buy choices across commercial, open-source, cloud-native, and partner platforms; lead architecture reviews, assessments, PoCs, and design workshops.

Presales, Solutioning & Customer Engagement

• Partner with sales and account teams to qualify opportunities, clarify customer outcomes, shape the pursuit strategy, and define the technical win themes.
• Lead discovery workshops with business and technology stakeholders; translate use cases, constraints, and non-functional requirements into solution architecture and a phased roadmap.
• Build customer-ready solution narratives, architecture diagrams, demo/PoC plans, implementation approaches, effort estimates, staffing models, assumptions, dependencies, risks, and commercial inputs.
• Own or contribute to RFI/RFP responses, proposals, statements of work, executive presentations, solution defenses, and technical due diligence.
• Design and deliver tailored demonstrations and PoCs that validate feasibility, business value, security, performance, user adoption, and scale.
• Present to CIOs, CTOs, CDOs, CISOs, enterprise architects, procurement teams, and partner stakeholders; handle objections and articulate trade-offs clearly.
• Collaborate with Microsoft, AWS, data-platform, and model providers on co-sell motions, reference architectures, funding opportunities, and joint solution plays.
• Support pursuit handoff to delivery with clear scope, acceptance criteria, architecture decisions, backlog, risks, and governance; capture reusable assets and lessons learned.

Data, Integration, MLOps & LLMOps

• Define secure data architectures for structured and unstructured enterprise data; integrate AI platforms with applications, APIs, databases, data lakes, SaaS platforms, and event-driven services.
• Define MLOps/LLMOps for development, deployment, CI/CD, model and prompt versioning, evaluation, observability, monitoring, and lifecycle management.
• Design evaluation approaches for quality, groundedness, hallucination, latency, cost, safety, reliability, and business outcomes; establish gateways, fallbacks, caching, rate limits, telemetry, and human escalation.

Responsible AI, Security & Governance

• Define architecture standards for Responsible AI and AI governance across the lifecycle.
• Address prompt injection, sensitive-data leakage, excessive permissions, hallucinations, model misuse, insecure integrations, and supply-chain risks.
• Embed identity, authorization, encryption, secrets management, logging, monitoring, privacy, data residency, and policy controls in solution designs.
• Partner with Security, Legal, Privacy, Risk, and Compliance teams to establish guardrails, review gates, and evidence for production approval.

Enterprise Architecture, Consulting & Delivery Leadership

• Translate business problems into prioritized AI use cases, architecture blueprints, technology roadmaps, and implementation strategies.
• Lead technical discussions with C-level executives, business leaders, engineering teams, customers, and technology partners.
• Provide architectural leadership from discovery and PoC through MVP, production deployment, adoption, and scaling.
• Identify reusable frameworks and accelerators; mentor AI/ML engineers, data engineers, developers, and solution architects.

Technology Landscape

Microsoft Azure: Copilot Studio; Azure AI Foundry; Azure OpenAI; Azure Machine Learning; Azure AI Search; Microsoft Fabric

AWS: Amazon Bedrock; Amazon SageMaker; Amazon Q; AWS Lambda; Amazon OpenSearch

AI Engineering & Data: OpenAI/GPT; Anthropic Claude; Llama; Hugging Face; LangChain; LlamaIndex; Semantic Kernel; Pinecone; Weaviate; Milvus; pgvector; Kubernetes

Required Qualifications

• 12+ years of software/technology experience, including 5+ years in AI/ML, data, or advanced analytics and 3+ years in solution, AI, ML, or data architecture responsibilities.
• Proven experience designing cloud-native enterprise solutions on Azure and/or AWS and implementing production Agentic AI/LLM solutions.
• Strong knowledge of LLMs, RAG, AI agents, prompt engineering, embeddings, vector databases, model evaluation, MLOps/LLMOps, and AI security/governance.
• Demonstrated presales experience in opportunity qualification, solution shaping, RFI/RFP responses, proposals, estimation, SOW development, demos/PoCs, and executive presentations.
• Ability to connect technical architecture to measurable business outcomes, defend solution choices, manage ambiguity, and influence stakeholders across business, technology, security, procurement, partners, and delivery teams.
• Strong programming understanding, preferably Python, with familiarity in APIs, microservices, containers, Kubernetes, enterprise integration, scalability, resiliency, and cloud design patterns.
• Excellent written, verbal, workshop-facilitation, storytelling, and presentation skills; willingness to travel as customer engagements require.

Preferred Qualifications

• Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
• Experience in AI-led consulting, digital transformation, enterprise modernization, Fortune 500 pursuits, or large strategic deals.
• Experience defining enterprise AI strategy, GenAI Centers of Excellence, multimodal AI, knowledge graphs, fine-tuning, and model optimization.
• Understanding of NIST AI RMF and Responsible AI practices; certifications from Microsoft, AWS, Databricks, or NVIDIA are advantageous.