Key Responsibilities
Agentic AI Solution Architecture
- Architect and deploy agent-based AI systems that automate complex workflows and decision-making processes across enterprise environments.
- Design multi-agent orchestration frameworks where agents collaborate, reason, and execute tasks autonomously.
- Translate customer business challenges into scalable Agentic AI architectures.
Customer-Facing Technical Leadership
- Serve as the primary technical advisor for enterprise customers during onboarding, solution design, and deployments.
- Conduct deep technical discovery sessions to understand customer use cases and requirements.
- Present platform capabilities, architectural solutions, and deployment strategies to engineering leaders, architects, and executive stakeholders.
Proof of Concept (POC) Development
- Lead and execute Agentic AI proof-of-concepts demonstrating real-world business value.
- Design and configure custom agents, workflows, and integrations tailored to customer environments.
- Deliver measurable outcomes and performance benchmarks that demonstrate ROI and operational impact.
Agent Orchestration & Autonomous Systems
- Implement and manage agent orchestration frameworks for enterprise-scale AI automation.
- Develop workflows involving multi-agent coordination, reasoning chains, memory, and task planning.
- Optimize agent decision-making pipelines and orchestration strategies.
Platform Deployment & Infrastructure
- Deploy Agentic AI platforms across enterprise infrastructure using cloud-native technologies.
- Implement scalable infrastructure leveraging:
- Kubernetes
- Serverless architectures
- Containerized deployments
- Infrastructure-as-Code
AI Platform Engineering
- Build resilient and scalable infrastructure to support high-performance AI agents and model orchestration systems.
- Integrate LLMs, vector databases, and agent frameworks into production environments.
Performance & Scalability Optimization
- Optimize system performance across:
- AI inference pipelines
- agent orchestration frameworks
- distributed infrastructure
- Ensure deployments are cost-efficient, reliable, and scalable.
Cross-Team Collaboration
- Lead collaboration across product, research, engineering, and customer teams.
- Manage and mentor distributed technical teams across offshore and onshore locations.
- Establish engineering best practices and ensure consistent technical standards.
AI Governance & Operational Excellence
- Establish best practices for:
- MLOps
- AIOps
- Model versioning
- Experiment tracking
- FinOps and infrastructure optimization
- Implement guardrails for responsible and secure AI deployments.
Innovation & Research
- Stay up to date with advancements in:
- Agentic AI
- LLM architectures
- autonomous systems
- reasoning frameworks
- Evaluate emerging tools and frameworks to enhance platform capabilities.
Documentation & Knowledge Sharing
- Produce comprehensive documentation covering:
- architecture designs
- deployment models
- integration workflows
- operational playbooks
Required Skills & Experience
AI / Machine Learning Expertise
- Strong understanding of AI/ML principles, deep learning concepts, and LLM architectures
- Hands-on experience building or deploying:
- Agentic AI systems
- autonomous agents
- multi-agent orchestration frameworks
- Experience implementing:
- LLM-based workflows
- RAG architectures
- AI reasoning pipelines
- agent tool integration
Agentic AI & LLM Ecosystem
Hands-on experience with modern AI frameworks such as:
- LangChain
- LangGraph
- AutoGen
- Model Context Protocol (MCP)
- OpenAI / Azure OpenAI
- Vertex AI
- Agent orchestration frameworks
- Vector databases
Experience building systems that leverage:
- reasoning agents
- tool-using agents
- multi-agent collaboration systems
Cloud & Infrastructure Expertise
Extensive experience deploying AI systems on major cloud platforms:
- AWS
- Google Cloud Platform
- Microsoft Azure
Hands-on experience with:
- Kubernetes
- Docker
- Serverless architectures
- Cloud AI services (Vertex AI, SageMaker, Bedrock)
- Distributed systems deployment
DevOps & Infrastructure Engineering
- Experience with CI/CD pipelines and automation
- Infrastructure-as-Code using tools such as:
- Experience implementing observability systems using:
- Prometheus
- Grafana
- ELK Stack
- Datadog
Strong understanding of:
- reliability engineering
- performance optimization
- distributed architecture scaling
Enterprise Integration & Architecture
- Strong understanding of:
- API design
- microservices architecture
- distributed systems
- data engineering workflows
- Experience integrating AI systems into enterprise platforms and business workflows.
Security & Compliance
Understanding of enterprise SaaS security practices including:
- SSO
- RBAC
- encryption and data protection
- API security
- compliance frameworks such as:
Customer Engagement & Communication
- Exceptional ability to communicate complex technical concepts to technical and non-technical stakeholders
- Proven experience delivering customer-facing AI solutions
- Strong relationship-building skills with enterprise clients.
Qualifications
- 10+ years of experience in software engineering, infrastructure engineering, or applied AI/ML engineering
- Strong proficiency in programming languages such as:
- Python
- TypeScript / JavaScript
- Deep understanding of:
- Agent development
- agent orchestration techniques
- distributed AI systems
- Experience building and deploying enterprise-grade AI platforms
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