Senior Agentic AI Engineer with Python (W2 Contract)

Hiring!!
Greetings from Modern Agile Technologies.

Position:   Senior Agentic AI Engineer (Python) 
Location:  Boston, MA (Onsite)

Type:        Contract (W2 Contract Only)

Primary Objective 

We are seeking a Senior Agentic AI Engineer to design, build, deploy, and operate enterprise-grade AI agents and multi-agent systems. The role focuses on Generative AI, LLMs, agentic workflows, RAG architectures, AI orchestration, and governed enterprise AI platforms—delivering scalable copilots, intelligent automation, and knowledge systems across onshore and offshore delivery environments. 

Success looks like: production-ready agents with measurable reliability, governed RAG integrated into enterprise systems, and clear observability/guardrails for business use cases. 

Key Responsibilities 

Primary 

·        Design, develop, and deploy AI agents and multi-agent workflows using Python and agentic frameworks (e.g., LangChain, LangGraph, or equivalent). 

·        Build enterprise-scale RAG solutions over structured and unstructured data sources. 

·        Develop agent orchestration workflows integrating models, tools, APIs, and enterprise services. 

·        Build AI-powered copilots, assistants, and automation solutions for enterprise use cases. 

·        Implement AI monitoring, observability, tracing, telemetry, and performance measurement. 

Must-Have Experience & Skills 

·        5–10 years of software engineering experience with strong Python expertise. 

·        3+ years designing and implementing AI/ML solutions. 

·        Hands-on experience building Generative AI applications using LLMs, RAG, and agentic frameworks (LangChain/LangGraph or equivalent; OpenAI SDK or similar). 

·        Experience delivering enterprise-grade AI solutions in production. 

·        Experience with distributed onshore/offshore team delivery. 

·        Solid API/service engineering fundamentals (REST/async services, integration patterns). 

·        Practical understanding of AI observability, evaluation, and production reliability. 

Preferred Skills 

·        Cloud AI platforms (Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar). 

·        Vector databases / search (pgvector, OpenSearch, Pinecone, Weaviate, or similar). 

·        LLMOps tooling (tracing, eval harnesses, prompt/version management). 

·        Enterprise integrations (SharePoint, Confluence, Salesforce). 

·        Containerized deployment practices (Docker; Kubernetes a plus). 

·        AI security, PII handling, and guardrail frameworks.