Artificial Intelligence Engineer

Description :

Design, build, and operate production grade agentic and GenAI systems end to-end. Deliver robust APIs, reusable components, and secure pipelines that connect LLMs with enterprise systems.

Combine strong software engineering with modern AI practices (RAG, agent orchestration, evaluation) to drive scalable business outcomes.

Key Responsibilities :

Agent & Application Engineering :

- Build multi agent systems (planning, tool use, delegation) using LangGraph or Semantic Kernel.

- Develop REST/gRPC APIs (FastAPI mandatory).

- Integrate tools, SQL, search, and document stores via Model Context Protocol (MCP).

- Connect with model gateways (OpenAI, Azure OpenAI, Bedrock, Vertex AI).

- Deliver pro code solutions (Python focus).

Retrieval, Data & Knowledge :

- Stand up RAG services with embeddings, hybrid/vector search (pgvector, Pinecone, Weaviate, OpenSearch).

- Build ingestion pipelines (Airflow, Prefect, Ray) for diverse enterprise data.

- Optimize retrieval quality with chunking, re rankers, query rewriting.

Quality, Testing & Evaluation :

- Apply evaluation frameworks (Promptfoo, RAGAs).

- Treat prompts/graphs as codeversion, diff, regression test.

- Track AI evaluation metrics for RAG applications.

Security & Compliance :

- Implement red teaming guardrails.

- Enforce policy chains and PII guardrails (OPA/Gatekeeper, Presidio).

Enterprise Integration :

- Ship connectors/events for SAP/CRM/ITSM and Kafka topics.

- Design idempotent, retry safe processors.

Tech Stack & Qualifications :

- Mandatory : Python, FastAPI, LangGraph/Semantic Kernel, one major cloud (Azure/AWS/GCP).

- Preferred : Containerization & Kubernetes (Helm/Argo CD), secondary languages (Java/Go/Node.js).

- Strong engineering mindset with focus on scalability, security, and evaluation.

Skills

Artificial Intelligence, Python, Machine Learning, Agentic AI, Generative AI, LLM, SQL