AI Engineer

Job Summary


Responsibilities

GenAI Agentic System Development

• Build agentic AI workflows using frameworks such as LangChain, LangGraph, and Haystack, including: multi-agent orchestration (planner, retriever, evaluator, executor agents); tool-calling, function execution, and system-to-system automation; memory management (short-term, long-term, and session-based).
• Implement Retrieval-Augmented Generation (RAG) pipelines using structured and unstructured enterprise data.
• Design hybrid search architectures combining Vector DBs and Graph DBs (e.g., Azure AI Search, Neo4j) for semantic, contextual, and relationship-based retrieval.

Enterprise Integration Cloud Engineering

• Develop and integrate AI-powered chatbots and agents within the Azure ecosystem, ensuring seamless interoperability with existing platforms and services.
• Integrate GenAI solutions with enterprise systems using APIs, event-driven architectures, and message brokers.
• Build secure, scalable backends leveraging Azure App Services, Azure Functions, Bot Framework, Azure Cache for Redis, and related services.
• Work closely with Cloud, Digital, Data Engineering, and Business teams to drive adoption and real-world impact.

Production Readiness, MLOps LLMOps

• Apply MLOps / LLMOps best practices across the lifecycle: model/version management and prompt versioning; CI/CD pipelines for GenAI applications; automated testing (prompt, retrieval, and regression testing); monitoring, logging, and observability for LLM outputs.
• Implement guardrails for safety, hallucination control, data privacy, and responsible AI.
• Ensure enterprise-grade governance, including access control, auditability, and compliance with internal policies.

Performance Optimization and Continuous Improvement

• Analyze chatbot and agent performance using quantitative and qualitative metrics (accuracy, latency, adoption, task completion).
• Optimize prompts, retrieval strategies, agent flows, and system performance based on real usage data.
• Drive continuous enhancement of user experience through experimentation and feedback loops.

Requirements

• Strong understanding of LLMs, transformers, embeddings, prompt engineering, and evaluation techniques.
• Experience building end-to-end GenAI/Agentic AI products, including backend services and frontend web apps.
• Hands-on experience with LangChain, LangGraph, n8n, Co-pilot for building modular, agent-based systems.
• Practical experience designing multi-agent architectures and orchestrating reasoning and action workflows.
• Strong experience with Vector Databases and Graph Databases (Azure AI Search, Neo4j, Databricks Vector DB) for hybrid, semantic and relationship-driven search.
• Proven experience implementing RAG pipelines with structured and unstructured enterprise data.
• Proficiency in Python, SQL, Spark, and familiarity with additional languages (e.g., JavaScript).
• Hands-on experience with PyTorch and TensorFlow.
• Experience working with high-performance, large-scale ML systems in production environments.
• Ability to solve complex problems in language understanding, reasoning, and GenAI system design.
• Experience deploying GenAI solutions on Azure, including: Azure Data Factory (ADF); Databricks; Azure AI Search; Databricks Genie; AI Document Intelligence; App Services, Azure Functions, Bot Framework; Azure Cache for Redis.

Disclaimer: This job posting has been aggregated from external source. Role details, content, and availability are subject to change. Applicants are advised to confirm the latest information directly on the company website before applying.