AI Developer

Benefits:

• 401(k)
• 401(k) matching
• Dental insurance
• Health insurance
• Paid time off

Title: Developer Standard I

Duration: 6 Months - Possible Extension

Location: Washington, DC (looking for local candidates)

Minimum Experience: 3 to 5 years (do not include your college period).

Hybrid Onsite: 4 days per week from Day 1, with a full transition to 100% onsite anticipated soon

Job Expectation: AI-Native Software Developer Profile (GenAI & Agentic AI, Full-Stack)

We seek developers with an AI-first engineering mindset, able to choose between deterministic code, LLMs, and agents, and to design for probabilistic outputs with robust fallback strategies while balancing cost, latency, and reliability. Core GenAI & LLM expertise includes integrating LLMs into production, prompt engineering, structured outputs, RAG pipelines, effective context management, and quality/hallucination evaluation.

Job Requirement:

• Rapid solution development skills: proficiency with modern AI development platforms, APIs, and tooling to quickly prototype, iterate, and deploy functional AI capabilities into real-world systems.

• Agentic AI experience: building autonomous/semi-autonomous agents with clear goals, constraints, orchestration patterns, tool-calling, state/memory handling, guardrails, and behavioural observability.

• Full-stack capabilities:

• Backend - API design (REST/GraphQL), secure services, data modelling, async workflows, external integrations.

• Frontend - modern frameworks (e.g., React), AI-native UX (streaming responses, explainability), state management, human-AI interaction patterns.

• Cloud-native experience in containerized/microservice environments, CI/CD for AI components, secure secrets handling, cost-aware designs, and performance optimization.

• Data & knowledge engineering: handling structured/unstructured data, building pipelines, preprocessing, embeddings, ranking, and versioning AI artifacts.

• Focus on reliability, security, and responsible AI: safeguard sensitive data, mitigate leakage/bias/safety risks, ensure auditability, and support graceful degradation.

• Excellent collaboration and communication: clear explanation of AI behaviour, cross-functional teamwork, documentation, and critical output review.

• High learning agility and craftsmanship: staying current with evolving AI tools, delivering production-quality systems.