AI Native Software Engineer

Overview

Build and ship production-ready software using AI as a core part of the engineering lifecycle, applying AI-native practices to develop and validate solutions and continuously improve how software is built and operated.

What you'll do

• Design and build production-grade agentic systems end-to-end: multi-agent orchestration, RAG pipelines, policy-based routing, tool invocation, memory management, and lifecycle observability.
• Build and own RAG pipelines: embeddings, chunking strategy, vector search, and context window engineering tuned against real quality targets.
• Integrate and abstract across multiple LLM providers (OpenAI, Anthropic, Vertex AI, and open-source models) with fallback routing and token, cost, and latency management.
• Implement LLMOps in production: eval harnesses with real quality metrics, prompt versioning, observability tooling (LangSmith, Braintrust, or equivalent), and cost and safety monitoring.
• Embed with client engineering teams to design, prototype, and deploy agentic solutions through workshops, proofs of concept, code-with sessions, and architecture walkthroughs.
• Build reusable patterns, accelerators, and playbooks that scale beyond individual client engagements.
• Define and use metrics to measure agent accuracy, latency, safety, and cost-effectiveness, and present findings and recommendations to client stakeholders in business terms.

What you'll need

• 7.5 to 10 years of software engineering experience in production environments.
• Minimum 1 year of hands-on experience designing and deploying agentic AI solutions in a production environment.
• Demonstrated experience with agentic orchestration frameworks such as LangGraph, CrewAI, AutoGen, or equivalent, at production depth.
• Direct experience calling LLM APIs (OpenAI, Anthropic, Vertex AI) in production code, including provider abstraction, token management, and latency and cost tradeoffs.
• RAG pipeline ownership: embeddings, chunking strategy, vector databases, and context engineering.
• LLMOps fundamentals: eval harness design, prompt versioning, and production observability.
• Cloud-native engineering maturity: Kubernetes, Docker, microservices, serverless, CI/CD, and IaC (Terraform or Helm).
• Strong Python, Java, or equivalent backend language skills, with production debugging and observability experience.
• Must have skill: Java Full Stack Development.
• 15 years full time education.

Details

• Location: Hyderabad.