About the Role

We're looking for a Rust developer who can bridge high-performance systems programming with modern AI/agentic engineering. This is a hybrid role: someone equally comfortable optimizing a graph algorithm in Rust as they are wiring that logic into an LLM-powered agent or API. Ideal for engineering-heavy domains (e.g., EPC, infrastructure optimization, industrial automation) where performance-critical computation and AI orchestration need to work together.

What You'll Do

- Design and implement performance-critical algorithms and services in Rust (graph algorithms, optimization routines, simulation engines, data pipelines)
- Benchmark and profile Rust implementations against alternatives (e.g., Python) to validate performance gains at scale
- Build bindings/interfaces (FFI, PyO3, gRPC, REST) so Rust-based compute modules can be called from AI agent frameworks and orchestration layers
- Integrate Rust services with LLM-based systems feeding structured outputs into agents built with LangGraph/LangChain or similar frameworks
- Design APIs and data contracts between high-performance Rust backends and AI/agent orchestration layers
- Write clean, memory-safe, well-tested, production-grade Rust (ownership/borrowing discipline, no unsafe unless justified)
- Collaborate with AI/ML engineers to identify which workloads should live in Rust vs. Python vs. an LLM call, based on latency, cost, and correctness needs
- Contribute to CI/CD pipelines (Azure DevOps or similar) for building, testing, and deploying Rust services in containerized environments

Required Skills

- Rust: 2+ years professional experience; strong grasp of ownership,



lifetimes, traits, concurrency (async/tokio or similar)
- Algorithms & data structures: comfortable implementing and optimizing graph algorithms (e.g., Dijkstra, A*), simulations, or numerical routines
- Python: working proficiency, enough to compare/benchmark against Rust implementations and integrate with AI/ML tooling
- AI/LLM integration experience: has built or integrated with agent frameworks (LangGraph, LangChain, or custom orchestration), or has hands-on experience calling LLM APIs (Claude, OpenAI, etc.) via SSE/streaming or batch
- APIs & interop: experience exposing Rust functionality via FFI, PyO3, gRPC, or REST/HTTP services
- Containerization: Docker experience; comfortable working in CI/CD pipelines

Nice to Have

- Experience with graph databases (Neo4j) or spatial/routing optimization problems
- Background in engineering, industrial, or infrastructure domains (EPC, energy, manufacturing)
- Familiarity with PostgreSQL/Redis for state and caching layers behind agentic systems
- Exposure to cloud container orchestration (Azure ACI/AKS or equivalent)
- Experience with Supabase Edge Functions or similar serverless compute for lightweight AI-adjacent services

What Makes This Role Different

Most "Rust developer" roles stop at systems programming; most "AI engineer" roles stop at prompting and orchestration. This role sits at the intersection — you'll be the person who makes sure the fast, correct, deterministic part of the system (Rust) and the versatile, reasoning part of the system (AI agents) actually talk to each other cleanly and perform well in production.

📌 Rust Developer (Telangana)
🏢 Orcapod Consulting Services
📍 Telangana