Senior Software Engineer -Python Full Stack +AI

Key Responsibilities:

• Full-Stack Development: Design and build scalable web applications, including responsive frontends (React, Next.js) and high-performance backends (Python, FastAPI, Node.js).
• AI & Agent Integration: Integrate LLMs, small language models (SLMs), and multi-agent workflows via APIs or custom orchestration frameworks.
• Data & Knowledge Pipelines: Manage relational databases alongside vector stores to support retrieval-augmented generation (RAG) and real-time context retrieval.
• AI-First UX: Design intuitive user experiences for AI interactions, including streaming responses, intermediate thought outputs, and error handling for non-deterministic AI behavior.
• Deployment & Operations: Implement CI/CD pipelines, containerization (Docker, Kubernetes), and monitoring tools to track model performance, latency, and operational costs in production.
• Lead the discussion with business users. Understand what they need, challenge it where needed, and turn it into a clear solution design.
• Set up proper evaluation for every solution. Measure accuracy, cost per request, token usage and response time then improve them.

Required Qualifications:

• Experience: 7+ years of professional software development experience, with a strong portfolio of applied AI applications (At least 5+ production deployed use cases).
• Frontend Skills: Proficiency in modern JavaScript/TypeScript, React, or Next.js.
• Backend Skills: Strong command of Python for backend services and API design (FastAPI, Flask).
• AI/ML Knowledge: Hands-on experience with OpenAI, Anthropic, or open-source LLMs, prompt engineering, vector databases (Pinecone, pgvector, Milvus), and RAG pipelines.
• Infrastructure: Working knowledge of cloud platforms (AWS, GCP, or Azure) and container tools like Docker.
• ERP Experience: Prior experience of working in SAP will be a plus

Preferred Skills:

• Experience building multi-agent systems or using agent frameworks (LangChain, Langgraph, LlamaIndex)
• Knowledge of MLOps and observability tools (LangSmith, Arize, Prometheus)
• Understanding of fine-tuning or self-hosting smaller open-source models