Senior Agentic AI Engineer (Python)_Boston

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.

Software Engineering, Artificial Intelligence (AI), Python Programming/Scripting Language, Automation, Artificial Intelligence (AI) Agents, Artificial Intelligence (AI), Use Cases, Business Case, System Integration (SI), Python Programming/Scripting Language, * 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