AI / Agentic AI Developer

Role Overview

We are seeking an Agentic AI Developer to design, build, and operate agent-based AI solutions that combine large language models (LLMs) with tools, workflows, and enterprise data to deliver measurable business outcomes. This role is hands‑on and engineering‑driven: you will prototype quickly, productionize reliably, and continuously improve agent performance through evaluation, observability, and iteration. You will work primarily in Python and leverage an open‑source AI stack while integrating with modern data platforms (including Databricks) and enterprise security standards.

Key Objectives

• Deliver production‑grade agentic AI applications (assistants, copilots, autonomous workflows) from discovery through deployment.
• Establish repeatable engineering patterns for tool use, retrieval‑augmented generation (RAG), memory, planning, and orchestration.
• Implement robust evaluation, monitoring, and safety guardrails to improve reliability, accuracy, and user trust.
• Integrate agents with enterprise systems and data platforms (APIs, databases, event streams, Databricks/Spark) while meeting performance, cost, and security requirements.

Primary Responsibilities

• Design and build agentic workflows that use LLMs plus tools (function calling), retrieval, and structured reasoning to accomplish business tasks end‑to‑end.
• Develop and maintain Python services and libraries for agent orchestration, tool routing, prompt/version management, and policy/guardrail enforcement.
• Build RAG pipelines: document ingestion, chunking, embedding generation, indexing, and retrieval; ensure relevance, freshness, and access control alignment.
• Integrate open‑source AI frameworks and components (e.g., LangChain/LangGraph, LlamaIndex, vLLM, Transformers, FastAPI, Pydantic) into a coherent production architecture.
• Implement evaluation and testing for agentic systems: offline benchmarks, golden datasets, regression tests, LLM‑as‑judge patterns (where appropriate), and online metrics tied to product KPIs.
• Operationalize observability: trace agent/tool calls, track latency and cost, monitor quality signals, detect retrieval/model drift, and set up alerting and feedback loops.
• Build secure integrations with enterprise tools and data sources (REST/gRPC services, SQL databases, data warehouses/lakes, vector databases), including secrets management and auditing.
• Collaborate with data engineering and platform teams to leverage Databricks/Spark for large‑scale data preparation, embedding jobs, batch/stream processing, and feature pipelines where relevant.
• Deploy and operate services using containers and CI/CD; ensure reproducibility, environment management, and reliable rollbacks across versions.
• Partner with product, UX, and stakeholders to translate ambiguous needs into agent behaviors, tool contracts, and measurable acceptance criteria.
• Document designs and contribute to engineering standards; mentor peers through code reviews, design reviews, and knowledge sharing.

Required Skills & Experience

• Strong software engineering experience building production services in Python (API design, testing, packaging, performance, and maintainability).
• Hands‑on experience building LLM applications, including prompt engineering, tool/function calling, RAG/embeddings, and multi‑step workflows.
• Comfort with open‑source AI stack and ecosystem (e.g., Hugging Face Transformers, sentence‑transformers, LangChain/LangGraph or LlamaIndex, vector databases such as FAISS/Chroma/Pinecone equivalent, MLflow or similar tracking).
• Strong SQL skills and understanding of data engineering fundamentals (batch vs. streaming, data quality, schema evolution, governance) and how they impact AI systems.
• Experience with evaluation approaches for LLM systems (quality metrics, test harnesses, human‑in‑the‑loop review, and reliability techniques).
• Experience deploying and operating services (Docker, Kubernetes or equivalent), CI/CD, and observability/monitoring practices.
• Ability to communicate tradeoffs clearly—balancing quality, latency, cost, reliability, and risk.

Preferred / Nice to Have

• Awareness of Databricks platform concepts (workspaces, notebooks, jobs, clusters), and experience using Spark for large‑scale ETL or embedding generation.
• Experience with Databricks MLflow Model Registry and/or Unity Catalog (or similar governance) for managing models, features, and data access.
• Experience serving open‑source LLMs (e.g., vLLM, TGI, llama.cpp) and optimizing inference (quantization, batching, caching).
• Experience with advanced agent patterns: planning, reflection, memory, tool selection, multi‑agent collaboration, and workflow graphs/state machines.
• Experience with security and responsible AI practices (PII handling, prompt injection defenses, access control, auditability, and safe tool execution).
• Experience building reusable platform components (SDKs, templates, reference architectures) to enable multiple teams.