Description

Embeds with client teams, understands manual business workflows, and builds AI-agent-powered automation to
replace them. Owns the solution end-to-end — discovery, build, deploy, iterate — working directly with clients with
minimal direction.
Agentic AI
• LLM API fundamentals — LLM API integration — tool use, function calling, streaming, context management
• Skill building — Design and publish reusable skills consumable by LLM-powered agent workflows
• MCP (Model Context Protocol) — Build and consume MCP servers; expose and integrate tools for agent use
• A2A communication — Multi-agent coordination, task delegation, result aggregation across agent networks
• RAG pipelines — Vector databases, chunking, embeddings, retrieval pipelines
• Prompt engineering — System prompts, chain-of-thought, evaluation and iteration
• Agent frameworks — Strands as primary framework; awareness of LangGraph, CrewAI, or AutoGen
• Guardrails — Hallucination mitigation, human-in-the-loop checkpoints, agent observability
Backend
• Python 3.10+, async/await, type hints
• FastAPI or equivalent Python web framework
• Basic SQL — query, filter, join; awareness of vector DBs (pgvector, Pinecone, OpenSearch)
• REST API design, JWT / OAuth2
Workflow Automation
• Decompose manual business processes into agent pipelines
• Document processing — PDF, Excel, unstructured data extraction and classification
• Enterprise system integration — SharePoint, email, Jira, SAP or similar
Infrastructure
• AWS AgentCore — Host and operate agent workloads; understand lifecycle, scaling, and invocation patterns
• AWS services — EC2/ECS, Lambda, RDS, S3 — support agent deployment and data storage needs
• Docker and containerisation
• GitHub Actions CI/CD
ADDITIONAL SKILLS
• Next.js 15+, TypeScript, Tailwind CSS
• Terraform / Infrastructure as Code
• Kubernetes
• RPA tools — UiPath, Power Automate
• Fine-tuning and model evaluation
• CRE domain knowledge
Proven experience in software engineering
• At least one production project involving LLM agents, skill building, or RAG pipelines
• Track record of owning and delivering solutions end-to-end with minimal guidance
• Experience working directly with non-technical stakeholders — gathering requirements, presenting solutions
• Consulting or client-facing background is an advantage

Key Skills

Education

Any Gradute