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Forward Deployement Engineer at Zohorecruit | JobCrexa | JobCrexa

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Z

Forward Deployement Engineer

Zohorecruit · India

FULL TIME

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.

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