4Bell Technology

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.