Senior AI Engineer

• 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.