Lead I - ML Engineering :: AI Engineer

Job Title: Senior AI Engineer Experience Range

7+ Years of professional software development experience Hands-on experience building AI/ML or LLM-based enterprise solutions

Hiring Location : Hyderabad Must-Have Skills AI / Generative AI

Hands-on experience building AI systems using Large Language Models (LLMs) Experience designing and developing:

Retrieval-Augmented Generation ( RAG ) pipelines Agentic AI workflows Multi-model orchestration

Experience integrating LLMs with enterprise data and tools Experience with agent frameworks and tool-use/connector patterns (e.g., MCP ) Prompt engineering fundamentals AI-assisted development tools (GitHub Copilot, Cursor, etc.)

Backend Development

Strong backend development experience REST API development Enterprise integration services Modern programming languages/frameworks (Python, Java, Node.js, etc.)

Cloud & Platform

Cloud-native application development AWS / Azure / GCP Secure application development Authentication & Authorization Secrets management

DevOps / CI-CD

GitLab CI/CD (or equivalent) Automated build, test, and deployment pipelines

AI Observability

Structured logging Monitoring AI system performance Cost monitoring Output quality monitoring Tracing & instrumentation

Software Engineering

Enterprise application architecture Scalable and maintainable software development Technical design Component-level architecture decisions Strong ownership mindset Ability to work in agile environments with evolving requirements

Good-to-Have Skills

High-throughput distributed systems Low-latency architecture LLM evaluation frameworks AI observability platforms Hallucination detection Agent tracing React / Frontend development AI for log analysis AI-driven anomaly detection Operational intelligence solutions Forward Deployed Engineering (FDE) experience Consulting/customer-facing engineering experience Multi-domain enterprise solution delivery

Preferred Technical Stack AI Technologies

LLMs RAG Agentic AI Multi-Agent Systems MCP (Model Context Protocol) Prompt Engineering

Cloud

AWS Azure Google Cloud Platform (GCP)

Backend

REST APIs Enterprise Integrations Microservices

DevOps

GitLab CI/CD Automation Pipelines

Observability

Logging Monitoring Tracing AI Performance Evaluation

Key Responsibilities

Design and build enterprise-grade AI applications using LLMs. Develop RAG pipelines and agentic AI workflows. Build backend APIs and integration services. Integrate AI capabilities with enterprise systems and data sources. Implement secure, scalable, cloud-native AI solutions. Build and maintain CI/CD pipelines. Monitor AI system performance, cost, and output quality. Apply AI engineering best practices for security, reliability, and compliance. Work closely with product, architecture, and engineering teams to deliver production-ready AI solutions.

Agentic AI, CI/CD, LLMs, GitLab, Backend Development, Authentication and Authorization, Cloud Infrastructure, Observability, REST