- As a software engineer you will be closely working with engineering team on scaling Agentic Ai capabilities.
- 8+ years of working experience in web-development or solutions architecture, with a proven track record of developing consumer-facing and internal solutions.
- Experience implementing large-scale technological enhancements and/or pivots, including pilot implementation and analysis.
- Strong experience, on Generative Ai, Agentic AI, LLM, RAG, Langhchain, Langhgraph
- Strong System Design skills
- Excellent understanding of DevOps; chef and puppet technique.
- Understanding of multiple programming languages, including at least framework
- Experience working with IT infrastructure and cloud development.
- Experience working with data and DBMS, including legacy and emerging database technologies.
- Experience with RDBMS, NoSQL.
- Experience building highly available customer-facing applications, in a GDHA setting.
- Experience building cloud-native applications.
- Experience with API's.
- Experience designing highly available and resilient solutions; ability to identify performance improvement opportunities and transform traditional monolith architecture to modern microservices-based loosely decoupled architecture.
- A bachelor's degree or foreign equivalent in computer science or a related field.
- Experience in 2 or 3 of the following technology areas: Infrastructure, Security, DevOps, Application Development, Database technologies, Cloud computing (AWS, Azure, GCP).
AI Platform & Technical Skills
Candidates should demonstrate knowledge and hands-on experience across the following enterprise AI architecture competencies:
AI Platform & Model Access
- Designing model consumption patterns (API, AI gateway, vendor managed AI).
- Understanding centralized vs embedded model access.
- Azure OpenAI, AWS Bedrock, provider-embedded AI (Salesforce, ServiceNow, Microsoft co-pilot studio).
AI Platform Architecture & MLOps
- Enterprise RAG architecture patterns; embedding lifecycle management.
- Agentic workflows, vector stores and enterprise capabilities integration.
- AWS SageMaker, Amazon Bedrock Agentcore and similar platforms.
- MLOps/LLMOps (MLFlow): prompt versioning, model registry, model gateway across lifecycle stages.
Agent & Orchestration Frameworks
- Agent vs workflow vs rules-based decisioning; single-agent vs multi-agent orchestration.
- Copilot Studio, Salesforce Agentforce, conceptual LangChain/LangGraph literacy.
