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AI Solutions Architect

Location: Bengaluru
Fulltime
Experience: 8-12 years

Role Overview:
We are seeking a visionary AI / Agentic AI Solutions Architect to join our enterprise AI practice at a senior leadership level. This is not a conventional AI engineering role — it is a strategic, customer-facing architecture position at the convergence of business transformation, enterprise systems, and next-generation Agentic AI.

The successful candidate will define how AI reshapes business operations, design enterprise-grade multi-agent orchestration frameworks, build production-ready Agentic AI solutions, and serve as a trusted thought leader for C-suite stakeholders across our customer portfolio. You will own the full lifecycle from AI strategy and discovery through reference architecture, PoC delivery, and scaled production deployment.

Key Responsibilities:
1. AI Solution Architecture:
• Architect end-to-end enterprise AI and Agentic AI solutions from discovery through deployment.
• Design multi-agent orchestration systems using frameworks such as LangGraph, CrewAI, AutoGen, and Semantic Kernel.
• Develop Retrieval-Augmented Generation (RAG) architectures integrating vector databases and enterprise data sources.
• Define AI integration patterns for SAP, Salesforce, Microsoft 365, ServiceNow, and other enterprise platforms.
• Create reusable reference architectures and AI solution blueprints.

2. Agentic AI & Automation:
• Design and deploy AI agents capable of reasoning, planning, and autonomous task execution.
• Build agent ecosystems with supervisor agents, tool calling, memory management, and human-in-the-loop controls.
• Integrate Agentic AI with RPA, BPM, ERP workflows, and intelligent document processing solutions.
• Define observability, monitoring, and governance mechanisms for enterprise AI systems.

3. Customer Engagement & Pre-Sales:
• Lead AI discovery workshops and executive discussions with CXO-level stakeholders.
• Translate business challenges into AI transformation roadmaps and solution strategies.
• Support RFPs, solution proposals, PoCs, and effort estimations.
• Serve as a trusted advisor during customer engagements.

4. Delivery & Governance:
• Lead PoC-to-production AI implementation journeys.
• Ensure scalability, security, compliance, and operational readiness of AI solutions.
• Implement Responsible AI and governance frameworks aligned with enterprise standards.
• Collaborate with engineering, data, security, and business teams for successful delivery.

5. Thought Leadership:
• Mentor AI engineering teams on Agentic AI and enterprise AI best practices.
• Contribute to reusable accelerators, frameworks, and innovation initiatives.
• Represent the organization in customer forums and industry events.

Technical Skills Required:
1. Generative AI & LLMs:
• Deep expertise in LLMs: OpenAI GPT series, Anthropic Claude, Google Gemini, Meta LLaMA, Mistral, Cohere, and open-source model families.
• Advanced prompt engineering: chain-of-thought, few-shot, tree-of-thought, ReAct, and structured output prompting techniques.
• LLM fine-tuning approaches: LoRA, QLoRA, PEFT, instruction tuning, and RLHF alignment strategies.
• Proficiency with LLM APIs, tokenization, context window management, and cost-performance optimization.

2. Agentic AI & Multi-Agent Frameworks:
• Hands-on expertise in agentic orchestration frameworks: LangGraph, LangChain, AutoGen (Microsoft), CrewAI, Semantic Kernel, Haystack, and custom agent loops.
• Architecture of multi-agent systems: supervisor-worker hierarchies, agent communication protocols, tool registries, and task decomposition strategies.
• Experience with Model Context Protocol (MCP), function calling, tool use APIs, and external system integrations for agent action spaces.
• Knowledge of agent memory architectures: episodic, semantic, procedural, and working memory patterns.

3. AI Infrastructure & Cloud AI Platforms:
• Azure AI Services, Azure OpenAI, Azure AI Foundry, Copilot Studio, and Azure Machine Learning.
• AWS Bedrock, SageMaker, Kendra, Q Business, and Lambda-based AI integration patterns.
• Google Vertex AI, Gemini API, Dialogflow CX, and Document AI.
• Multi-cloud AI strategy, model gateway architectures (LiteLLM, Portkey, Azure APIM), and cloud cost governance for AI workloads.

4. Data & Knowledge Architecture:
• RAG pipeline design: document ingestion, chunking strategies, embedding models, hybrid search, and re-ranking layers.
• Vector database expertise: Pinecone, Weaviate, Qdrant, Chroma, Milvus, Azure AI Search, and pgvector.
• Knowledge graph integration: Neo4j, Amazon Neptune, GraphRAG, and ontology-based reasoning frameworks.
• Enterprise data platform integration: Snowflake, Databricks, Azure Synapse, dbt, and streaming architectures (Kafka, Event Hubs).

5. Enterprise System Integration:
• SAP S/4HANA, BTP, ABAP APIs, and AI extensions via SAP AI Core / Joule.
• Salesforce Einstein AI, Agentforce, Apex integrations, and CRM data grounding for AI agents.
• Microsoft ecosystem: SharePoint, Teams, Power Platform, Copilot extensibility, and Graph API.
• ServiceNow AI and automation APIs, ITSM/HRSD integration patterns, and Now Assist configurations.

6. AI Engineering & Development:
• Python proficiency: LangChain, LlamaIndex, FastAPI, Pydantic, asyncio, and AI SDK ecosystems.
• API-first design, RESTful and GraphQL integration, OAuth 2.0/OIDC security, and event-driven architectures.
• Containerization and orchestration: Docker, Kubernetes, Helm charts, and CI/CD pipelines for AI model deployment.
• AI observability and monitoring: LangSmith, Langfuse, Arize AI, Weights & Biases, Helicone, and custom telemetry pipelines.

Experience Requirements:
1. 10–15 years of experience in enterprise technology, including at least 4 years in AI/GenAI architecture roles.
2. Proven experience designing and deploying enterprise AI and Agentic AI solutions.
3. Strong customer-facing consulting and solution architecture experience.
4. Exposure to AI transformation programs across industries such as Manufacturing, BFSI, Retail, Healthcare, or Shared Services.
5. Experience managing AI PoC-to-production engagements.

Preferred Qualifications:
1. Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or related field.
2. Relevant certifications in Azure AI, AWS ML, or Google Cloud AI.
3. Experience in consulting firms, enterprise AI product companies, or system integrators preferred.
4. Thought leadership contributions such as whitepapers, conferences, or open-source AI contributions are a plus.

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Think you’re the right fit? We’d love to hear from you.

Email: info@kdservicess.com