AI Developer

Role Summary

The AI Developer will build and support production-ready Generative AI solutions—including RAG-based assistants and agentic workflows—that integrate with enterprise data and applications. You will work with Tech Leads/Architects to translate solution designs into secure, scalable implementations with strong engineering practice, observability, and reusable components.

Key Responsibilities

• Build GenAI apps: Implement LLM-based features such as Q&A, summarization, extraction, and classification using prompt engineering and structured outputs.
• Implement RAG pipelines: Build ingestion + chunking + embeddings + retrieval flows using vector databases to ground answers in enterprise knowledge.
• Develop agent workflows: Create agentic automations (tool calling, task routing, multi-step workflows) using common frameworks/patterns.
• Enterprise integration: Integrate AI services with internal systems via APIs, auth, and approved access controls; follow enterprise engineering standards.
• Quality & monitoring: Add logging/telemetry and participate in evaluation/testing to catch regressions and ensure stable production behavior.
• Developer productivity: Use coding assistants (e.g., Copilot/Cursor-style) to accelerate development while maintaining clean code and reviews.

Experience

• 5+ years total IT/software engineering experience (enterprise applications, APIs, services).
• 2+ years hands-on AI/ML/GenAI experience, including building or supporting AI solutions beyond
• Must-Have Technical Skills
• Python (strong); ability to build services, scripts, and automation.
• Experience with LLMs (APIs/providers) and understanding of risks like hallucinations and quality control.
• Hands-on RAG framework experience (vector DB + retrieval + grounding loop).
• Exposure to Agents / agentic AI patterns (tool calling, orchestration, task decomposition).
• Solid engineering fundamentals: APIs, version control, testing, troubleshooting, secure coding practices.
• Experience with MCP servers or MCP-style tool integrations.

Preferred / Nice to Have (Keep Optional for Hiring)

• Familiarity with FastAPI/Flask, Docker, CI/CD, and production monitoring patterns.
• Exposure to financial services or regulated data environments.

Behavioral Competencies

• Strong ownership and problem-solving; can translate requirements into working, testable software.
• Collaborative—works effectively with architects, product owners, and operations partners.
• Continuous learning mindset in GenAI and automation.