What Youll Do:

- Design and develop RAG pipelines, agentic workflows, and multi-model orchestration that solve high-value, domain-specific use cases across lines of business.
- Build and optimize the APIs, services, and integration layers that connect AI capabilities to enterprise data and systems.
- Partner with US-based FDEs to turn prioritized, ROI-driven opportunities into production-ready builds.
- Implement agentic AI systems that improve reasoning, tool use, and interaction across complex, multi-step workflows, working within defined architectural patterns and reference implementations.
- Contribute to and utilize CI/CD pipelines (GitLab or equivalent) to enable automated build, test, and deployment workflows.
- Implement instrumentation, structured logging, and tracing to monitor AI system performance, cost, and output quality, in partnership with platform and SRE teams.
- Leverage AI-assisted development tools to improve code quality, accelerate development, and enhance debugging.
- Contribute design input within defined architectural frameworks and evaluate trade-offs at the component or feature level.
- Collaborate with product, architecture, and engineering partners to translate business requirements into scalable technical solutions.
- Apply and help uphold AI engineering standards for performance, security, reliability, and compliance across deployed solutions.

What Youll Bring:

- 8+ years of professional software development experience, including hands-on experience building AI/ML or LLM-based systems.
- Experience designing and building RAG pipelines, agentic workflows, or multi-model orchestration.
- Experience developing backend services, REST APIs, and enterprise integration solutions using contemporary languages and frameworks.
- Experience with cloud platforms (AWS/Azure/GCP)



and cloud-native application development.
- Experience integrating LLMs and agent frameworks with enterprise data and tooling, including connector/tool-use patterns (e.g., MCP) and retrieval and grounding strategies.
- Experience building and operating CI/CD pipelines (GitLab or equivalent).
- Experience incorporating observability into software solutions, including monitoring of AI system performance, cost, and output quality.
- Understanding of secure application design (authentication/authorization patterns, secrets handling).
- Ability to operate effectively in environments with evolving requirements.
- Strong ownership mindset and clear technical communication skills.
- Experience using AI-assisted development tools to improve productivity and engineering outcomes.

Must Have Skills:

- Hands-on experience building AI systems using LLMs RAG, agentic workflows, or multi-model orchestration.
- Experience with cloud platforms (AWS/Azure/GCP) and cloud-native development.
- Experience developing backend services, REST APIs, and integration layers that connect AI capabilities to enterprise systems.
- Experience with agent and tool-use frameworks, including connector patterns (e.g., MCP).

Nice-to-Have:

- Experience designing high-throughput, low-latency distributed systems.
- Experience with prompt engineering and LLM evaluation frameworks.
- Experience with AI observability output evaluation, hallucination detection, or agent tracing.
- Familiarity with frontend technologies (e.g., React) for building end-user AI experiences.
- Experience leveraging AI for log analysis, anomaly detection, or operational insights.
- Experience working in a forward-deployed, consulting, or customer-facing engineering model.
- Experience delivering solutions across multiple lines of business or domains, matching engineering effort to business value.

📌 Sr Engineer, AI (Hyderabad)
🏢 TMUS Global Solutions
📍 Hyderabad