Emids Technologies - AI Engineering Lead/Architect

About the Role :

We are seeking a hands-on AI Engineering Lead/Architect to design, build, and scale production-grade conversational AI platforms and intelligent services.

This role requires a blend of deep technical expertise, system architecture knowledge, and strong engineering leadership.

The ideal candidate will lead the end-to-end development of AI-powered products, from experimentation and prototyping to scalable production deployments.

You will work closely with product managers, data scientists, and engineering teams to develop robust LLM-powered applications, APIs, and AI pipelines that power conversational experiences across voice and messaging channels.

This role requires strong backend engineering, distributed systems expertise, and cloud-native architecture skills, along with hands-on experience integrating modern AI technologies into enterprise platforms.

Key Responsibilities :

AI Platform Architecture :

- Design and architect scalable AI systems and conversational AI platforms for production environments.

- Build robust LLM-powered services, AI pipelines, and intelligent APIs that support enterprise applications.

- Define architecture for multi-channel conversational experiences across chat, voice, and messaging platforms.

- Ensure high availability, scalability, security, and performance of AI systems.

AI & LLM Engineering :

- Develop and integrate Large Language Model (LLM) based applications, including prompt orchestration, tool usage, and context management.

- Build RAG (Retrieval-Augmented Generation) pipelines, vector databases, and knowledge retrieval frameworks.

- Design and implement conversation management systems, dialogue orchestration, and intent recognition pipelines.

- Integrate AI models with backend services, APIs, and enterprise platforms.

Backend & Platform Development :

- Design and build high-performance APIs and microservices to support AI-driven applications.

- Architect distributed systems and event-driven architectures for real-time AI workloads.

- Implement scalable backend services using modern cloud-native architectures.

- Develop frameworks for AI observability, monitoring, and performance optimization.

Data Engineering & Real-Time Processing :

- Build and manage data pipelines for training, evaluation, and analytics of AI systems.

- Work with real-time streaming platforms and analytics systems for conversational data.

- Ensure data governance, privacy, and secure handling of sensitive datasets.

Voice & Conversational Technologies :

- Integrate Speech-to-Text (STT) and Text-to-Speech (TTS) technologies into conversational platforms.

- Build voice-enabled AI systems supporting real-time conversations and automation.

- Develop integrations with multichannel messaging platforms such as chat, voice, SMS, and messaging apps.

Technical Leadership :

- Provide technical leadership and architectural guidance to engineering teams.

- Lead design reviews, architecture decisions, and code quality standards.

- Mentor engineers and drive best practices for AI engineering, DevOps, and cloud-native development.

- Collaborate with cross-functional teams to translate business requirements into scalable AI solutions.

Required Skills & Qualifications :

Experience :

- 8+ years of software engineering experience with strong backend development expertise.

- Proven experience building production-grade AI/ML systems.

- Hands-on experience implementing LLM-powered applications and conversational AI platforms.

Technical Skills :

- Strong programming skills in Python, Java, or Node.js.

- Experience with API design, microservices architecture, and distributed systems.

- Hands-on experience with cloud platforms (AWS / Azure / GCP).

- Experience working with containerization and orchestration tools (Docker, Kubernetes).

- Experience with vector databases, knowledge retrieval systems, and AI orchestration frameworks.

- Experience building event-driven architectures using message brokers or streaming systems.

AI/ML Expertise :

- Experience integrating LLMs, conversational AI frameworks, and NLP systems.

- Strong understanding of AI model lifecycle, evaluation, and deployment.

- Experience building RAG pipelines and knowledge-based AI assistants.

Data & Analytics :

- Experience designing data pipelines, analytics platforms, and real-time data processing systems.

- Familiarity with data storage technologies such as relational and NoSQL databases.

Communication & Leadership :

- Strong technical leadership and architectural decision-making skills.

- Ability to work in fast-paced environments and lead complex engineering initiatives.

- Excellent communication and cross-team collaboration skills.