Senior Agentic AI Engineer (Python)_Boston
At a glance
Senior AI Engineering role at Fa Ertb Saasfaprod1. United States · full-time · $48,000–$168,000 base.
aimlroles summary, based on the employer's posting.
What you'll do
- Build and launch Python agents and multi-agent workflows, then operate them in production
- Create RAG systems across structured and unstructured data, connecting them to enterprise platforms and knowledge stores
- Orchestrate models, tools, APIs and enterprise services for copilots, assistants and automation
- Track agent behavior with tracing, telemetry and performance measures, and add memory, guardrails and security controls
- Improve prompts and workflows with data science and ML teams, evaluating reliability and execution performance
What you bring
- Five to ten years in software engineering, with strong Python skills
- At least three years building AI or machine-learning solutions
- Hands-on work with LLM applications, RAG and agent frameworks, plus production delivery of enterprise AI
- Experience delivering with distributed onshore and offshore teams, alongside solid API and service engineering skills
- Practical knowledge of evaluating AI systems, monitoring them and maintaining production reliability
Who this fits
This senior role suits an engineer who can take enterprise AI delivery from design through production and work across distributed teams. The work is described as onsite or offshore, with flexibility, and is available to independent contractors.
From the employer
Senior Agentic AI Engineer (Python)
Experience: 5–10 years
Location: Onsite / Offshore (Flexible)
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.
Also expected
- Implement agent memory patterns (short-term, long-term, episodic).
- Integrate agents with enterprise platforms (APIs, databases, SharePoint, Confluence, Salesforce, knowledge repositories).
- Contribute to AI governance: guardrails, security controls, policy enforcement, and compliance.
- Optimize prompts, reasoning strategies, workflows, and execution performance.
- Collaborate with Data Science and ML teams on evaluation, optimization, and continuous improvement.
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.
Soft Skills
- Clear written and verbal communication with technical and business stakeholders.
- Ability to own delivery end-to-end in a distributed team model.
- Pragmatic trade-off judgment between speed, quality, cost, and governance.
Compensation, Benefits and Duration
Minimum Compensation: USD 48,000
Maximum Compensation: USD 168,000
Compensation is based on actual experience and qualifications of the candidate. The above is a reasonable and a good faith estimate for the role.
Medical, vision, and dental benefits, 401k retirement plan, variable pay/incentives, paid time off, and paid holidays are available for full time employees.
This position is available for independent contractors
No applications will be considered if received more than 120 days after the date of this post