Lead Al Engineer

Namely; Mountain View (CA); On-site; USD 19,000 - 35,000; Full time

Job summary

Namely is seeking a Lead AI Engineer in Hyderabad to architect, develop, and scale enterprise AI infrastructure. You will lead design for RAG frameworks, LLM gateways, and agentic workflows, ensuring robust data pipelines integrated with AWS cloud.

Responsibilities include guiding system design, enforcing JSON-based validation, and mentoring teams while advancing scalable, enterprise-grade AI platforms.

Qualifications

• 5-8 years of professional software engineering experience.
• Deep, hands-on expertise in Java and Spring/Spring Boot; Python for AI/ML integrations.
• Strong knowledge of relational databases (PostgreSQL/MySQL) and MongoDB.
• Extensive knowledge of object-oriented design and enterprise integration patterns.
• Hands-on experience with AWS and containerized applications.

Responsibilities

• Design, deploy, and optimize RAG frameworks and centralized LLM gateways.
• Lead architectural oversight for content ingestion and enrichment pipelines.
• Build and orchestrate multi-agent workflows for enterprise AI integrations.
• Implement Model Context Protocol to standardize interactions with foundation models.
• Architect highly reliable LLM generation pipelines with strict JSON outputs for schema validation.
• Provide technical leadership across cloud deployments and infrastructure planning.

Skills

Java & Spring Boot

Python

AWS cloud services

RAG

LLM orchestration

JSON-based data validation

PostgreSQL

MySQL

MongoDB

Distributed systems

Tools

Docker

Kubernetes

AWS

Job description

Job Title: Lead AI Engineer Location: Hyderabad

About the Role:

We are seeking an experienced Lead AI Engineer to architect, develop, and scale our enterprise AI infrastructure and content processing pipelines. You will lead the technical design of AI systems, focusing on Retrieval-Augmented Generation (RAG) frameworks, LLM gateways, and intelligent agentic workflows. As a technical leader, you will ensure our AI data pipelines are robust, highly structured, and seamlessly integrated into our AWS cloud ecosystem. In this role you will...

AI Architecture Strategy:

Lead the architectural oversight and technical direction for content ingestion and enrichment pipelines, ensuring scalable and efficient AI integrations.

RAG LLM Integration:

Design, deploy, and optimize advanced Retrieval-Augmented Generation (RAG) frameworks and centralized LLM gateways.

Agentic Workflows:

Build and orchestrate multi-agent platforms and workflows using modern AI integration frameworks.

Protocol Implementation:

Drive the implementation of Model Context Protocol (MCP) to standardize and streamline interactions between foundation models and enterprise backend systems.

Data Pipeline Engineering:

Architect highly reliable LLM generation pipelines, enforcing strict structured JSON outputs to guarantee schema validation and application parsing stability.

Technical Leadership System Design:

Mentor engineering teams, apply best-in-class software design patterns, and guide infrastructure deployment planning across cloud environments.

Experience:

5-8 years of professional software engineering experience, with a proven track record of designing and deploying enterprise-grade systems and AI platforms.

Programming Frameworks (Mandatory):

Deep, hands-on expertise in Java and the Spring / Spring Boot framework. Good working knowledge of Python is also required for AI/ML integrations and scripting.

Database Expertise (Mandatory):

Strong knowledge of both relational databases (RDBMS such as PostgreSQL or MySQL) and NoSQL databases, specifically MongoDB.

Design Patterns (Mandatory):

Extensive knowledge of object-oriented design, architectural design patterns, and enterprise integration patterns for building scalable, decoupled systems.

Cloud Infrastructure (Mandatory):

Hands-on experience with AWS cloud services, including the deployment, scaling, and management of containerized applications and data pipelines.

AI/ML Expertise:

Practical experience building scalable RAG applications, vector search optimizations, and LLM orchestration layers.

Data Engineering:

Proven ability to build resilient data ingestion workflows with a strong emphasis on JSON-based structured data validation over legacy text formats.

Good to have skills:

Familiarity with enterprise AI toolkits (such as Google ADK). Experience managing regional infrastructure rollouts and navigating model availability constraints across different deployment stages.

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