AI Engineering Lead — Agentic AI & Cloud

AI Engineering Lead — Agentic AI & Cloud

Company: Dasceq
Location: Remote prefer Delhi NCR, Kolkata
Experience: 4–6 years
Employment: Full-time
Proposed compensation: ₹75000 - 150000, negotiable depending on hands-on engineering, architecture and client-delivery experience

About Dasceq

Dasceq is developing a next-generation Agentic AI platform that combines intelligent customer conversations, predictive analytics, workflow automation, omnichannel communication and business-system integrations.

Our platform supports inbound and outbound AI agents, voice calls, automated dialing, SMS, email, campaign management, payment workflows, customer relationship management (CRM) and data-driven decisioning.

Initially developed for accounts receivable and debt collection, the platform is expanding into additional industries such as home services, field service dispatch, customer support, sales, lead qualification and business process automation.

Our vision is to enable companies to manage customer interactions and workflows using intelligent AI agents integrated directly into their existing business systems.

Role Overview

We are looking for a highly hands-on Technical Delivery Manager who combines strong AI/ML engineering capabilities with software architecture, system integration, project implementation and client-facing leadership.

This individual will be responsible for understanding client requirements, designing solutions, leading development, managing technical resources and ensuring successful client implementations.

The ideal candidate should be technically strong enough to independently develop complex AI components, commercially aware enough to understand business requirements, and confident enough to lead technical discussions and demonstrations with clients.

This is a hands-on technical leadership role, not a project coordination or people-management-only position.

The AI Engineering Lead will own Dasceq's end-to-end technical architecture, cloud infrastructure, application deployment, integrations and platform reliability.

AWS & Cloud Architecture

• Design, implement and manage AWS infrastructure for a scalable, multi-tenant SaaS platform.
• Work with services such as EC2, ECS/Fargate, Lambda, S3, RDS, API Gateway, CloudWatch, IAM and networking/VPC.
• Evaluate Amazon Bedrock and other LLM providers based on product requirements, cost and performance.
• Implement secure networking, encryption, secrets management, access controls and backup strategies.
• Optimize cloud infrastructure for reliability, scalability and cost.

DevOps & Production Deployment

• Own CI/CD pipelines, Docker-based deployments and infrastructure as code.
• Manage development, staging and production environments.
• Establish deployment, rollback, monitoring and incident-response procedures.
• Implement application logging, alerting, performance monitoring and automated testing.
• Coordinate database migrations, upgrades and production releases.
• Manage disaster recovery planning and infrastructure documentation.

Application & Integration Architecture

• Design backend services, APIs, databases and event-driven workflows.
• Lead integration with CRM, payment providers, telephony platforms, dialers and scheduling systems.
• Build reusable connectors and configurable client-specific workflows.
• Maintain secure multi-tenant architecture with appropriate customer-data isolation.
• Manage technical performance, integration reliability and API security.

Client Implementation

• Lead client discovery, technical solutioning and implementation planning.
• Own deployment, integration, testing, user acceptance and go-live.
• Conduct technical demonstrations and client progress meetings.
• Manage developers, external contractors and implementation dependencies.
• Provide post-launch technical support and continuous improvement.

Hands-On Engineering & Leadership

• Personally develop and review production-quality Python/backend and agentic AI code.
• Lead technical architecture, code reviews and engineering standards.
• Manage junior developers, analysts and implementation resources.
• Make build-versus-buy decisions for AI models, infrastructure, orchestration and integrations.
• Own production stability, cloud costs, technical documentation and delivery quality.

Required Experience: 4–6 years in software/backend/AI engineering, including hands-on AWS deployment experience, production coding, API integrations and technical project ownership.

Experience deploying multi-tenant SaaS platforms and AI applications is strongly preferred.

Pay: ₹75,000.00 - ₹150,000.00 per month

Application Question(s):

• Current CTC

Expected CTC

Work Location: In person