Lead I - ML Engineering :: AI Engineer

Role Description 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.

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