AI Engineer

Role Overview

We are seeking a versatile AI Engineer to design, architect, build, and operationalize secure, scalable, and production-grade applications with AI embedded within business services. The role is intentionally broad and fluid: the engineer will work across front-end, back-end, APIs, cloud AI services, and intelligent automation based on evolving business priorities. In addition to Generative AI solutions, the engineer is expected to build RPA bots enhanced with AI when automation opportunities arise.

Primary Focus: Full-stack AI applications, Generative AI, RAG, agentic solutions, AI document processing, intelligent automation and RPA, cloud AI architecture, secure integration, and production operations.

Key Responsibilities

• Design and build end-to-end enterprise applications with AI capabilities integrated into business services and workflows.
• Develop responsive front-end applications using React and create secure back-end services using C#/.NET Core, Python, and REST APIs.
• Design end-to-end enterprise AI and Generative AI solutions aligned with functional, security, integration, performance, availability, and cost requirements.
• Create high-level and detailed solution architectures, architecture decision records, data flows, API contracts, deployment patterns, and technical specifications.
• Build Retrieval-Augmented Generation (RAG), AI agents, copilots, document intelligence, conversational AI, and intelligent workflow solutions.
• Build RPA bots and intelligent automation workflows, integrating AI capabilities such as document understanding, classification, extraction, summarization, and decision support where relevant.
• Architect and implement Azure-based solutions using Azure OpenAI, Azure AI Foundry, Azure AI Search, Azure AI Document Intelligence, Azure Functions, Service Bus, API Management, Storage, and AKS.
• Architect and implement AWS-based solutions using Amazon Bedrock, Bedrock Agents and Knowledge Bases, Lambda, S3, OpenSearch, API Gateway, ECS, or EKS.
• Evaluate foundation models and select models based on quality, latency, safety, data residency, throughput, and cost.
• Develop secure REST APIs, microservices, and event-driven processing components using C#/.NET Core and Python.
• Implement prompt management, grounding, structured outputs, guardrails, content safety, evaluation, observability, and hallucination-reduction controls.
• Build CI/CD and Infrastructure-as-Code pipelines for repeatable provisioning, testing, and deployment across environments.
• Partner with product, engineering, security, architecture, compliance, and business teams to move solutions from discovery and proof of concept into production.
• Conduct design reviews, troubleshoot complex technical issues, document engineering decisions, and mentor junior engineers.

Required Technical Skills

• Strong full-stack application development experience, including React, JavaScript/TypeScript, C#/.NET Core, Python, REST APIs, and service integration.
• Experience designing applications in which AI capabilities are embedded within user-facing and back-end business services rather than delivered only as standalone models or prototypes.
• Strong experience in AI solution design and architecture, including cloud-native, microservices, event-driven, API-led, and containerized patterns.
• Hands-on experience with Generative AI, LLMs, prompt engineering, embeddings, vector search, RAG, tool/function calling, and agent orchestration.
• Practical experience with Azure AI services and AWS Bedrock services in enterprise solution delivery.
• Hands-on experience building RPA bots and workflow automations using a leading RPA platform, with the ability to integrate bots with APIs, enterprise applications, and AI services.
• Experience with FastAPI or similar API frameworks, ASP.NET Core Web API, OAuth 2.0, JWT, managed identities, RBAC, secrets management, and secure networking.
• Experience with relational and NoSQL data stores, vector databases, caching, object storage, and search platforms.
• Working experience with Docker, Kubernetes, Git, Azure DevOps or GitHub Actions, and Terraform, Bicep, or CloudFormation.
• Understanding of AI evaluation, model monitoring, prompt/version management, telemetry, responsible AI, privacy, and governance.
• Ability to work across changing priorities and select the appropriate engineering approach for AI applications, full-stack development, API integration, or RPA automation.

Qualifications

• Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related discipline.
• 4-6 years of professional software engineering or AI engineering experience.
• Demonstrated experience designing and delivering full-stack applications and AI solutions on Azure and/or AWS.
• Experience taking applications, AI solutions, or automation bots through design, development, testing, security review, deployment, monitoring, and operational support.
• Strong analytical, documentation, stakeholder communication, and technical problem-solving skills.

Preferred Experience

• Azure AI Engineer Associate, Azure Solutions Architect, AWS Solutions Architect, AWS Machine Learning, or relevant application development certification.
• Experience with Microsoft Copilot Studio, Azure AI Foundry Agent Service, Amazon Bedrock Agents, or multi-agent frameworks.
• Experience with React-based enterprise applications, ASP.NET Core services, API gateways, and modern authentication patterns.
• Experience with Power Automate Desktop, UiPath, Automation Anywhere, or equivalent RPA platforms, including AI-assisted automation use cases.
• Experience with document processing, voice AI, human-in-the-loop review, workflow orchestration, or enterprise automation.
• Knowledge of FinOps practices and cost optimization for AI workloads.
• Experience working in regulated or security-conscious enterprise environments.

Success Measures

• Applications and architectures are secure, supportable, scalable, and aligned with enterprise standards.
• AI capabilities are effectively integrated into business services and deliver measurable quality, reliability, latency, and cost performance.
• RPA bots are reliable, maintainable, auditable, and appropriately enhanced with AI where it improves business outcomes.
• Proofs of concept are converted into production-ready implementations with complete operational controls.
• Technical documentation, testing evidence, deployment automation, and monitoring are complete and reusable.
• Risks, assumptions, dependencies, and design trade-offs are clearly communicated to stakeholders.

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.