Custom Software Engineer

Role Summary

Build high quality agentic AI applications—implement LLM tooling, RAG pipelines, vector search, and API integrations—delivering rapid prototypes that evolve into production systems. Own hands on development, testing, and integration, with a portfolio that demonstrates speed, experimentation, and learning. AI Powered Tech Talent.

Responsibilities

Hands on Build

• Implement agents (tools, skills), prompts/templates, planners, memory, and evaluation loops write clean, testable code and CI/CD pipelines.
• Stand up RAG services (ingestion, chunking, embeddings, indexing) and integrate vector DBs add observability and guardrails.

Integration

• Connect to enterprise APIs, event streams, and data sources ensure robust error handling, retries, and fallbacks.

Quality & Safety

• Write unit/integration tests implement offline/online evals, red team scenarios, and content/action safety boundaries.

Iteration & Communication

• Prototype quickly, demo frequently, and communicate trade offs and results clearly with stakeholders.

Must Have Qualifications (AI Native Archetype)

• Strong CS and software engineering fundamentals.
• Hands on experience with LLMs, RAG, vector DBs, and AI APIs.
• Proven ability to build end to end applications and integrate AI into systems.
• Visible portfolio of AI projects demonstrating experimentation and speed.
• Fast learner with a bias for rapid prototyping and iteration.
• Able to translate business problems into practical AI solutions with clear communication.

Programming Languages (Developer)

Core build proficiency (choose two or more):

• Python (agent frameworks, data/RAG services, evaluation, notebooks to services)
• TypeScript/Node.js (service wrappers, tool servers, API integration, front end hooks)
• Java (enterprise services, Spring Boot, concurrency/performance)

Nice to have

• C#/.NET (enterprise stacks)
• Go (high throughput agents/services)
• Rust (performance critical tooling)
• SQL (PostgreSQL/pgvector, query optimization)
• Bash (DevOps)

Frameworks & tooling

LangChain, LlamaIndex, Semantic Kernel vector DBs (pgvector, Pinecone, Weaviate, Milvus) test frameworks (PyTest/JUnit/Jest) containers, serverless, and cloud SDKs.