Mid AI Engineer

Job description

**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. **Required Qualifications** **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.