Agentic AI Developer

Job Summary:

We are seeking a passionate and highly skilled Solution Architect Generative AI & Agentic AI to design, develop, and deploy intelligent systems leveraging large language models (LLMs), multiagent frameworks, and enterprise AI integration patterns. This role will play a key part in building next-generation AI applications that autonomously reason, act, and learn across complex business workflows.As a Generative & Agentic AI Solution Architect you will own the end-to-end architecture for enterprise-grade GenAI solutions and multi-agent systems. Youll translate business objectives into scalable, compliant, and observable AI capabilitiesspanning data pipelines, foundation-model ops, Retrieval-Augmented Generation (RAG), and autonomous agent orchestration. You will partner with product owners, data scientists, platform engineers, security, and business stakeholders to accelerate AI-driven transformation while safeguarding performance, ethics, and regulatory compliance.

Key Responsibilities:

1.

• Design and implement applications using LLMs (e.g., OpenAI, Mistral, Claude, Llama)
• Build autonomous multi-agent systems using frameworks like AutoGen, LangGraph, CrewAI, or AgentOps
• Develop scalable backend services and orchestration pipelines using Python/Node.js integrated with AI APIs
• Lead discovery workshops, create MVP backlogs, and convert PoCs into production-ready solutions following MLOps / LLMOps best practices
• Oversee model selection, finetuning, prompt-engineering, evaluation, and continuous monitoring

2.

• Translate business requirements into agentic workflows with reasoning, memory, and tool usage
• Integrate GenAI and agents with enterprise systems (e.g., SAP, ServiceNow, Salesforce) via APIs and RAG pipelines
• Work with vector databases (e.g., Pinecone, FAISS, Weaviate) for semantic search and long-term memory
• Define reference architectures for GenAI (LLMs, Diffusion, SSMs) and agentic patterns (task-decomposition, planner-executor, tool-calling)
• Design secure micro-service and event-driven topologies on cloud / hybrid infra (AWS Bedrock, Azure OpenAI, Google Vertex, private GPUs)

• Required

  Technical

  Competencies:

Generative

• Agentic Frameworks: Experience designing goal-oriented agents using AutoGen, CrewAI, Semantic Kernel, or custom planners; familiarity with tool-calling, memory management, and AI schedulers
• LLMOps / MLOps: CI/CD for model artifacts, feature stores, vector DBs (Pinecone, Weaviate, FAISS), model gateways, and policy engines (Guardrails, Azure AI Safety)
• Cloud & DevSecOps: Terraform, Kubernetes, Docker, serverless, GPU orchestration, secret management, SAST/DAST integration
• Programming: Python (primary), TypeScript/Node, Bash; strong design-pattern discipline and code-review leadership
• Build replaceable but working proofofconceptsCLI, Streamlit, or VS Code Jupyterso stakeholders can touch” the idea
• Write production-grade Python/TypeScript to orchestrate agents with AutoGen / CrewAI / Semantic Kernel
• Hook agents to real systems (SAP BAPIs, Salesforce APIs, REST/GraphQL) and manage auth tokens & secrets
• Build CI/CD (GitHub Actions, Terraform, Helm) that auto-tests prompts, pushes Docker images, and provisions GPUs
• Instrument tracing (OpenTelemetry) and set up dashboards for tokens, latency, cost per call

Education:

• Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, Engineering, or a related field

Note: