Artificial Intelligence Engineer

Job Title: AI Developer

Location: Remote

Contract Type: Long-Term Contract

Job Description

We are seeking a Senior AI Developer with strong expertise in building agentic AI systems, scalable RAG architectures, and production-grade LLM observability. The role involves designing and implementing multi-agent workflows, retrieval pipelines, evaluation frameworks, and safety-aligned AI capabilities across cloud environments (Azure, AWS, GCP).

Key Responsibilities

Build and optimize agentic workflows using LangGraph, CrewAI, AutoGen, LlamaIndex Agents, and related frameworks.

Develop scalable RAG pipelines with vector databases (Qdrant, Pinecone, Weaviate, Milvus, FAISS) and hybrid retrieval.

Implement observability using Langfuse and OpenTelemetry for full agent/LLM traceability.

Create LLM evaluation pipelines using DeepEval, Ragas, TruLens, or similar tools.

Design robust memory systems for agents (short-term, long-term, vector memory).

Ensure safety and guardrails for tool execution and policy enforcement.

Deploy AI systems using Kubernetes, automated CI/CD, and cloud-agnostic design patterns.

Integrate AI agents with enterprise systems (Salesforce, ServiceNow, SAP, Workday, Slack, Teams).

Required Skills

Agentic Frameworks: LangGraph, CrewAI, AutoGen, LlamaIndex.

RAG Optimization: Vector search, hybrid retrieval, HNSW indexing, metadata filtering.

Observability: Langfuse, OpenTelemetry.

LLM Evaluation: DeepEval, Ragas, TruLens.

Memory Architecture: Short-term, long-term, vector memory; conflict-free state management.

AI Safety: Schema enforcement, reversible tool execution, guardrails.

Cloud & DevOps: Kubernetes, Docker, CI/CD pipelines (GitHub Actions/GitLab/Jenkins).

Enterprise Integration: APIs with Salesforce, ServiceNow, SAP, Workday, Slack, Teams.

Programming: Strong Python engineering skills.

Cloud Platforms: Cloud-agnostic architecture across Azure, AWS, GCP.

Ideal Candidate Profile

Proven experience in agentic AI development and RAG architectures.

Hands-on expertise in LLM observability and evaluation frameworks.

Strong background in cloud-native deployments and enterprise integrations.

Ability to design safe, scalable, and production-ready AI systems.