Senior AI Engineer

Job description

- 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.