LLM Agentic AI Solution Architect

Job Title:

LLM Agentic AI Solution Architect Job Type:

FTE Job Description: Experience – Minimum 12 years Primary Skills – Azure OpenAI, Azure AI Studio, Azure and GCP Cloud Functions, Kubernetes, LLM orchestration, LLM Architecture, Retrieval-Augmented Generation (RAG), APIs and custom connectors Integration Educational Qualification Bachelor's or Master's degree in Computer Science, Data Science, or a related field. Experience Range 10-12 years of experience in AI/ML-related roles, with a strong focus on LLM's & Agentic AI technology. Primary (Must have skills) - To be Screened by TA Team Generative AI Solution Architecture

(2–3 years): Proven experience in designing and architecting GenAI applications, including Retrieval-Augmented Generation (RAG), LLM orchestration (LangChain, LangGraph), and advanced prompt design strategies. Backend & Integration Expertise (5+ years): Strong background in architecting Python-based microservices, APIs, and orchestration layers that enable tool invocation, context management, and task decomposition across cloud-native environments (Azure Functions, GCP Cloud Functions, Kubernetes). Enterprise LLM Architecture (2–3 years): Hands-on experience in architecting end-to-end LLM solutions using Azure OpenAI, Azure AI Studio, Hugging Face models, and GCP Vertex AI, ensuring scalability, security, and performance. RAG & Data Pipeline Design (2–3 years): Expertise in designing and optimizing RAG pipelines, including enterprise data ingestion, embedding generation, and vector search using Azure Cognitive Search, Pinecone, Weaviate, FAISS, or GCP Vertex AI Matching Engine. LLM Optimization & Adaptation (2–3 years): Experience in implementing fine-tuning and parameter-efficient tuning approaches (LoRA, QLoRA, PEFT) and integrating memory modules (long-term, short-term, episodic) to enhance agent intelligence. Multi-Agent Orchestration (2–3 years): Skilled in designing multi-agent frameworks and orchestration pipelines with LangChain, AutoGen, or DSPy, enabling goal-driven planning, task decomposition, and tool/API invocation. Performance Engineering (2–3 years): Experience in optimizing GCP Vertex AI models for latency, throughput, and scalability in enterprise-grade deployments. AI Application Integration (2–3 years): Proven ability to integrate OpenAI and third-party models into enterprise applications via APIs and custom connectors (MuleSoft, Apigee, Azure APIM). Governance & Guardrails (1–2 years): Hands-on experience in implementing security, compliance, and governance frameworks for LLM-based applications, including content moderation, data protection, and responsible AI guardrails. Key Responsibilities: Architect Scalable GenAI Solutions: Lead the design of enterprise architectures for LLM and multi-agent systems, ensuring scalability, resilience, and security across Azure and GCP platforms. Technology Strategy & Guidance: Provide strategic technical leadership to customers and internal teams, aligning GenAI projects with business outcomes. LLM & RAG Applications: Architect and guide development of LLM-powered applications, assistants, and RAG pipelines for structured and unstructured data. Agentic AI Frameworks: Define and implement agentic AI architectures leveraging frameworks like LangGraph, AutoGen, DSPy, and cloud-native orchestration tools. Integration & APIs: Oversee integration of OpenAI, Azure OpenAI, and GCP Vertex AI models into enterprise systems, including MuleSoft Apigee connectors. LLMOps & Governance: Establish LLMOps practices (CI/CD, monitoring, optimization, cost control) and enforce responsible AI guardrails (bias detection, prompt injection protection, hallucination reduction). Enterprise Governance: Lead architecture reviews, governance boards, and technical design authority for all LLM initiatives. Collaboration: Partner with data scientists, engineers, and business teams to translate use cases into scalable, secure solutions. Documentation & Standards: Define and maintain best practices, playbooks, and technical documentation for enterprise adoption. Monitoring & Observability: Guide implementation of AgentOps dashboards for usage, adoption, ingestion health, and platform performance visibility. Soft skills/other skills: Communication Skills:

Communicate effectively with internal and customer stakeholders Interpersonal Skills:

Strong interpersonal skills to build and maintain productive relationships with team members & customer representatives Problem-Solving and Analytical Thinking:

Capability to troubleshoot and resolve issues efficiently. Task/ Work Updates : Prior experience in working on Agile/Scrum projects with exposure to tools like Jira/Azure DevOps

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