Forward Deployement Engineer

Zohorecruit; Haveli; On-site; INR 1,800,000 - 3,200,000; Full time

Job summary

Zohorecruit is seeking a senior AI engineer in India to partner with business leaders, product teams, and operations to define high-value AI use cases and implement scalable AI and automation solutions. You will design generative AI and agent-based systems, build RAG pipelines, and integrate AI services across enterprise platforms to streamline underwriting, claims, and customer service workflows.

You will drive MVP definitions, success metrics, and implementation roadmaps while mentoring teams

Qualifications

• Strong proficiency in Python and modern software engineering practices.
• Hands-on experience with Generative AI technologies, LLMs, and AI agents.
• Experience building RAG pipelines using vector databases and enterprise content repositories.
• Strong knowledge of Azure AI services, Azure OpenAI, Azure Functions, and cloud-native development.
• Experience with REST APIs, microservices, containers, and CI/CD pipelines.
• Familiarity with model deployment, monitoring, evaluation frameworks, and MLOps practices.

Responsibilities

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

Skills

Python

Generative AI

LLMs

AI agents

RAG pipelines

Azure AI services

Azure OpenAI

Azure Functions

Cloud-native development

REST APIs

Microservices

CI/CD

MLOps

Model deployment

Monitoring/evaluation frameworks

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.