About the Role :

We are seeking an experienced AI Forward Deployment Engineer to lead the successful design, deployment, adoption, and scaling of Generative AI and Agentic AI solutions for enterprise customers.

This role sits at the intersection of AI Engineering, Solution Architecture, Customer Success, Product Innovation, and Business Transformation. You will work directly with business leaders, engineering teams, and AI practitioners to identify high-value AI opportunities and transform them into production-grade solutions delivering measurable business outcomes.

You will act as a trusted advisor throughout the AI lifecyclefrom discovery and solution design to deployment, adoption, governance, optimization, and value realization.

Key Responsibilities :

AI Strategy & Customer Engagement :

- Engage with business stakeholders to identify AI transformation opportunities.

- Conduct AI discovery workshops and use-case assessments.

- Translate business challenges into AI-powered solutions.

- Build AI roadmaps aligned with enterprise objectives.

- Present AI architecture, ROI, and implementation strategies to executive leadership.

AI Solution Architecture :

- Design scalable Generative AI, Agentic AI, and Multi-Agent architectures.

- Architect enterprise AI platforms leveraging:

1. LLMs

2. RAG systems

3. AI Agents

4. Vector databases

5. Knowledge Graphs

6. Enterprise Integrations

- Define security, compliance, governance, and scalability requirements.

- Create reusable AI reference architectures and deployment frameworks.

AI Engineering & Deployment :

- Lead end-to-end deployment of AI applications from prototype to production.

- Build AI-powered workflows and intelligent automation solutions.

- Develop:

1. RAG applications

2. Agentic workflows

3. Copilot solutions

4. Conversational AI systems

5. AI-powered decision systems

- Integrate AI solutions with ERP, CRM, HRMS, ITSM, Finance, Procurement, and enterprise platforms.

Agentic AI Development :

- Design and deploy:

1. Autonomous Agents

2. Multi-Agent Systems

3. AI Orchestration Frameworks

4. Tool-Calling Agents

5. Workflow Automation Agents

- Define evaluation frameworks for agent performance.

- Implement memory, planning, reasoning, and orchestration capabilities.

AI Adoption & Success :

- Drive enterprise adoption and value realization.

- Establish success metrics and KPI frameworks.

- Conduct enablement workshops and executive briefings.

- Guide customers through organizational AI transformation.

- Build customer success playbooks and deployment methodologies.

AI Governance & Responsible AI :

- Implement AI governance frameworks.

- Ensure compliance with:

1. GDPR

2. ISO 42001

3. NIST AI Framework

4. Responsible AI principles

- Establish monitoring and observability for AI systems.

- Define model risk management and audit mechanisms.

Product & Innovation Feedback:

- Gather customer insights and market feedback.

- Influence product roadmaps and AI platform capabilities.

- Identify emerging AI trends and opportunities.

- Contribute to reusable accelerators, frameworks, and best practices.

Required Qualifications :

Education :

- Bachelor's or Master's degree in:

1. Computer Science

2. AI/ML

3. Data Science

4. Information Technology

5. Engineering

Experience:

- 8+ years in Solution Architecture, AI Engineering, Consulting, or Technical Leadership.

- 3+ years deploying Generative AI solutions.

- Experience delivering enterprise-scale technology transformations.

- Experience in customer-facing technical leadership roles.

Technical Skills:

AI & Machine Learning:

- Generative AI

- Large Language Models (LLMs)

- Prompt Engineering

- Fine-Tuning

- RAG Architecture

- Vector Databases

- Multi-Agent Systems

- AI Agents

- AI Evaluation Frameworks

- AI Safety and Governance

AI Platforms:

- Experience with:

1. OpenAI

2. Azure OpenAI

3. Anthropic Claude

4. Google Gemini

5. AWS Bedrock

6. Hugging Face

Agent Frameworks:

- LangGraph

- LangChain

- CrewAI

- AutoGen

- Semantic Kernel

- LlamaIndex

Programming:

- Python

- JavaScript / TypeScript

- REST APIs

- SDK Development

Cloud & Infrastructure:

- Azure

- AWS

- GCP

- Docker

- Kubernetes

- CI/CD

Data Technologies:

- SQL

- NoSQL

- Vector Databases

- Data Pipelines

- Data Governance

Preferred Qualifications:

- AI/ML certifications

- Cloud certifications (AWS/Azure/GCP)

- Experience with enterprise SaaS platforms

- Experience building AI Centers of Excellence

- Experience leading AI transformation programs

- Knowledge of FinOps, DataOps, MLOps, and AIOps