Senior AI Engineer (Agentic AI)

Our client, a leading enterprise enterprise, is seeking a Senior AI Engineer specializing in Agentic Workflows and LLM Integration . This specialized engineering role sits at the cutting edge of AI innovation, commanding an organization-wide mandate to design, deploy, and own multi-step autonomous agent systems.

The successful candidate will build robust backend infrastructures, orchestrate tool-calling logic, and manage advanced retrieval architectures to deliver resilient, production-grade AI applications within an enterprise framework. Key Accountabilities AI Architecture & Agent Workflow Engineering

Agent Execution Patterns: Design, build, and test highly complex, multi-step agent workflows utilizing established advanced architectural design patterns such as ReAct, planner-executor, and complex tool-chaining.

LLM Core Integration: Integrate flagship Large Language Models (including Anthropic Claude and OpenAI) with legacy enterprise APIs and internal microservices, engineering robust fault tolerance for retries, edge cases, and degraded ecosystem states.

Orchestration & Failure Resilience: Implement programmatic tool calling, function orchestration pipelines, and automated compensating actions to guarantee agent workflows remain stable under catastrophic or unexpected third-party failure conditions.

Human-in-the-Loop Controls: Architect and deploy conditional human-in-the-loop validation frameworks, including automated executive approvals, smart escalations, and exception-handling logic mandated by business governance or risk considerations.

Data Engineering, Prompts & Retrieval (RAG)

Context & Memory Architecture: Build and manage advanced agent memory retention layers and data retrieval mechanisms utilizing vector databases and Retrieval-Augmented Generation (RAG), tuning indexing schemas to ensure relevant context.

Prompt Management: Develop, maintain, optimize, and version-control complex prompt logic, semantic routing rules, and supporting technical documentation in accordance with strict enterprise engineering standards.

Production Deployment, Security & Observability

Cloud Operations: Deploy mission-critical AI services into production cloud environments, actively monitoring logs, distributed traces, and telemetry metrics to rapidly isolate and patch behavioral anomalies.

Enterprise Governance: Ensure all deployed solutions strictly mirror enterprise-grade security controls, identity management requirements, and rigorous data governance protocols.

Reliability Engineering: Partner with QA and Core Operations teams to continually upgrade automated test coverage, build out operational runbooks, establish incident response protocols, and drive system performance optimizations.

Requirements Education & Experience

Technical Tenure: Minimum of 4 years of hands-on experience developing backend or service-based software architectures using C# and/or Python .

AI Specialization: At least 1 year of production-level experience working directly with large language models, structured prompt engineering frameworks, or agentic AI-enabled systems.

Analytical Reasoning: Elite debugging skills with a proven capacity to reason across distributed APIs, asynchronous data flows, and non-deterministic AI system behaviors.

Technical Skills (Required)

Programming Ecosystems: Production-grade fluency in C# and/or Python , including async workflow patterns, service building, and automated test frameworks.

Cloud Platform: Microsoft Azure (encompassing compute, scalable storage, IAM identity models, and automated deployment pipelines).

LLM Integration & Tooling: Direct integration with Claude and/or OpenAI APIs (handling tool calling, prompt tokenization, rate limit mitigation, and state error handling).

Agent Orchestration Frameworks: Experience using Azure AI Foundry or Microsoft Agent Framework . Hands-on knowledge of LangGraph or LangChain is highly valued.

Vector Architectures: Experience with Pinecone or equivalent vector databases (handling structural indexing, query retrieval, and semantic relevance tuning).

APIs & Identity: RESTful API design, enterprise-grade service authentication, and secure service-to-service integrations.

Observability Suites: Distributed application logging, performance metrics tracking, and transaction tracing in production scales.

Preferred Qualifications (Desirable)

Containerization deployments utilizing Docker and Azure Container Services (AKS/ACA).

Experience architecting relational databases ( SQL Server ) and NoSQL document stores ( Azure Cosmos DB ).

Prior experience operating within highly regulated, compliance-driven, or audit-heavy corporate environments.

Foundational understanding of emerging AI Governance and AI Security (OWASP Top 10 for LLMs) paradigms.

Originally posted on Himalayas