AI Agent Developer (Python + GenAI)

Job Title and Overview We are hiring an AI Agent Developer (Python + GenAI) to design, build, and productionize LLM-powered agents that automate knowledge work and deliver measurable business outcomes. You will work closely with product, data, and platform teams to develop reliable agent workflows (tool-use, planning, retrieval, and evaluation) and deploy them into real customer-facing applications. Location & Work Mode Work Mode: Remote (work from anywhere). Location: Remote role; no specific city requirement. You will collaborate with a globally distributed engineering team across multiple time zones with overlap hours. Key Responsibilities - Build and iterate on AI agents in Python using modern orchestration frameworks (e.g., LangGraph/LangChain, LlamaIndex, Semantic Kernel) with robust tool-calling and guardrails. - Implement Retrieval-Augmented Generation (RAG) pipelines, including chunking strategies, embeddings, vector search, re-ranking, and citation/grounding workflows. - Design agent tool interfaces for internal and external systems (REST/gRPC services, SQL databases, SaaS APIs), including auth, retries, idempotency, and rate-limit handling. - Develop prompt templates, function-calling schemas, and structured outputs (JSON schema validation) to improve reliability and reduce hallucinations. - Set up evaluation and monitoring: offline eval harnesses, golden datasets, A/B prompt tests, and production telemetry (latency, cost, quality, safety signals). - Deploy services as containerized microservices (Docker) and support CI/CD, testing, and release processes; optimize inference performance and cost. - Collaborate in agile rituals (sprint planning, reviews), contribute to technical design docs, and participate in code reviews to maintain high engineering standards. - Required Skills & Experience - Experience with strong Python fundamentals (typing, async, packaging, testing). - Hands-on experience building with LLMs/GenAI (OpenAI, Azure OpenAI, Anthropic, Google, or open-source models) and understanding of token/cost/latency trade-offs. - Experience with RAG and vector databases/search (Pinecone, Weaviate, Milvus, pgvector, Elasticsearch/OpenSearch). - API development with FastAPI (preferred) or Flask; ability to design clean interfaces and integrate third-party APIs. - Solid grasp of software engineering practices: unit/integration tests (pytest), code quality, version control (Git), and documentation. - Familiarity with containers and deployment workflows (Docker, basic Kubernetes or managed cloud services). - Working knowledge of databases and data access patterns (PostgreSQL/SQL, caching with Redis). Education & Certifications Bachelor’s degree in Computer Science, Software Engineering, Data Science, or a related field (or equivalent practical experience). Preferred certifications (nice to have): AWS Certified Developer, AWS/Azure AI Engineer, or vendor GenAI credentials (e.g., OpenAI/Azure OpenAI learning paths).