Forward Deployement Engineer

Zohorecruit; Haveli Lakha; On-site; PKR 1,800,000 - 3,000,000; Full time

Job summary

Zohorecruit is seeking an experienced AI engineer to partner with business leaders, product owners, and operations to identify high-value AI use cases and translate requirements into scalable AI and automation solutions. You will design, build, and deploy Generative AI and agent-based solutions across enterprise platforms.

You will run workshops, define MVP scope with KPIs, and own end-to-end production including deployment, monitoring, and continuous improvement.

Qualifications

• Proficient in Python and software engineering practices.
• Hands-on with Generative AI, LLMs, and AI agents.
• Experience building RAG pipelines with vector stores and enterprise data sources.
• Strong knowledge of Azure AI services, Azure OpenAI, and cloud-native development.
• Experience with REST APIs, microservices, containers, and CI/CD pipelines.
• Familiarity with model deployment, monitoring, and MLOps practices.

Responsibilities

• Partner with business leaders and teams to identify high-value AI use cases.
• Conduct workshops to understand workflows, pain points, and objectives.
• Translate requirements into scalable AI and automation solutions.
• Define MVP scope, KPIs, and roadmaps for implementation.
• Design, build, and deploy Generative AI and agent solutions.
• Develop RAG applications leveraging enterprise knowledge sources.
• Build intelligent agents for underwriting, claims, and service workflows.
• Integrate AI services with enterprise platforms and data repositories.

Skills

Python

Generative AI

LLMs

RAG pipelines

Azure AI

APIs & Microservices

CI/CD

MLOps

Containers

Tools

LangChain

LangGraph

SemanticKernel

AutoGen

CrewAI

PromptEngineering

Job description

• Partnerwith business leaders, product owners, and operational teams to identifyhigh-value AI use cases.


• Conductworkshops and discovery sessions to understand workflows, pain points, andbusiness objectives.


• Translatebusiness requirements into scalable AI and automation solutions.


• DefineMVP scope, success criteria, KPIs, and implementation roadmaps.


• Design,build, and deploy Generative AI and Agentic AI solutions.


• DevelopRAG (Retrieval Augmented Generation) applications leveraging enterpriseknowledge sources.


• Buildintelligent agents capable of automating underwriting, claims, customerservice, IT support, and operational workflows.


• IntegrateAI services with enterprise platforms, APIs, databases, SharePoint,ServiceNow, CRM, and document repositories.

Required Qualifications

Technical Skills

• Strongproficiency in Python and modern software engineering practices.


• Hands-onexperience with Generative AI technologies, LLMs, and AI agents.


• Experiencebuilding RAG pipelines using vector databases and enterprise contentrepositories.


• Strongknowledge of Azure AI services, Azure OpenAI, Azure Functions, andcloud-native development.


• Experiencewith REST APIs, microservices, containers, and CI/CD pipelines.


• Familiaritywith model deployment, monitoring, evaluation frameworks, and MLOpspractices.

AI & Agent Frameworks

Experiencewith one or more:

• LangChain


• LangGraph


• SemanticKernel


• AutoGen


• CrewAI


• PromptEngineering and Evaluation Frameworks

Platform Integration & Deployment

• DeployAI models and applications into Azure cloud environments.


• Buildsecure and compliant integrations aligned with enterprise governancestandards.


• Configuremonitoring, observability, logging, and performance metrics.


• Supportproduction deployment and operational readiness activities.

Production Ownership

• Ownthe end-to-end success of deployed AI solutions.


• Troubleshootproduction issues and optimize model performance.


• Improvesolution accuracy, latency, scalability, reliability, and cost efficiency.


• Establishfeedback mechanisms and continuous improvement processes.

Stakeholder Engagement

• Collaboratewith business executives, architects, developers, data engineers, andsecurity teams.


• Presentsolution architectures, progress updates, and business value realizationmetrics.


• Facilitateadoption and change management activities.


• Mentorinternal teams on AI engineering best practices.


• Continuouslyidentify new AI opportunities within underwriting, claims, riskmanagement, customer service, and corporate operations.


• Prototypeemerging AI capabilities and demonstrate proof-of-value.


• Recommendreusable AI assets, frameworks, and accelerators.


• Supportstrategic AI roadmap development and future-state architecture.