Job Description

Hands-on Experience: Proven track record in building and deploying AI agents using frameworks such as LangChain, Strands, or cloud-native ADKs (Agent Development Kits).

CSP Foundation Models: Practical experience working with major Cloud Service Provider (CSP) AI ecosystems (e.g., AWS Bedrock, Google Vertex AI, Azure OpenAI Service).

Agent Capabilities: Deep knowledge of agentic architecture patterns (ReAct, multi-agent coordination, tools/function calling, memory retention, and RAG architectures)

Multi-Cloud Exposure: Hands-on experience navigating and deploying into at least one primary cloud provider (AWS, GCP, or Azure), with general operational knowledge across a multi-cloud estate.

Infrastructure as Code (IaC): Proficient in writing modular, reusable Terraform scripts for cloud infrastructure management.

DevOps Practice: Experience configuring CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Azure DevOps, Jenkins) for both standard application code and AI/ML artifacts.

Problem Solver: Proven ability to rapidly build POCs from rough business requirements to prove technical feasibility.

Collaborative Team Player: Excellent communication skills with a track record of actively collaborating with competency centers, engineering leads, and business teams.

Experience with vector databases (e.g., Pinecone, Qdrant, PGVector, Milvus).

Familiarity with Docker, Kubernetes, or serverless execution models (AWS Lambda, Azure Functions, Cloud Run) for lightweight agent hosting.

Exposure to agent monitoring, evaluation frameworks, and LLM observability tools

Core Languages: Strong proficiency in Java or .NET (C#) for writing core enterprise service layers and microservice

SDLC Discipline: Full-lifecycle experience, including system design, unit testing, automated integration testing, and code reviews.