4Bell Technology

Staffing & Recruiting

AI-Solution Architect(R-2140)

1,000,000.00-5,500,000.00/A

B.Tech/B.E

IT (Information Technology)

Full-time

Bangalore/Bengaluru

14-Nov-2026

Generative AI LLM Agentic AI Cloud AI Infrastructure Solution Architect

Job Description

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.