Principal AI Engineer – Automation & AI

eClinical Solutions•Mansfield, MA

8d•$160,000 - $200,000

About The Position

The Principal AI Engineer – Automation & AI is a hands-on technical leader responsible for designing, building, and deploying AI-powered automation solutions across eClinical Solutions. The role is builder-first, with 70–80% hands-on development, and works within a small team of 2–4 engineers. The engineer partners directly with the CTAO and business leaders to identify valuable use cases and quickly deliver production AI systems. It is an implementation-focused role rather than a program management or oversight position, with continuous building, shipping, and iteration using modern AI tools and agentic frameworks. The role develops agentic workflows, RAG-based applications, document intelligence and summarization pipelines, and workflow automation for enterprise functions including R&D, Engineering, Professional Services, IT, Finance, Sales, and Marketing. It owns delivery from translating business problems into working systems through architecture, prompt strategies, orchestration, API integrations, data pipelines, testing, validation, monitoring, and stakeholder adoption. The engineer prioritizes work by business impact, feasibility, and speed to value, and delivers weekly or bi-weekly releases. The position also builds reusable AI assets and contributes to lightweight standards for security, data handling, responsible AI, evaluation, and performance monitoring. It provides technical guidance to a small team, leads by example through hands-on work, and helps develop the AI engineering function as it grows. Employees are expected to use company-approved AI tools appropriately, follow company policies, protect confidential information, validate AI-generated content, and exercise professional judgment. Employees remain accountable for their work.

Requirements

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field.
6–10+ years of software engineering experience, including recent hands-on work in AI/LLM systems.
Proven experience building and deploying LLM-powered applications, agentic workflows, and production automation solutions.
Strong hands-on experience with Codex, Claude Code, Gemini, NotebookLM, or similar AI-assisted development tools.
Strong hands-on experience with LangChain, LangGraph, LlamaIndex, or equivalent frameworks.
Proficiency in Python.
Proficiency in API development and system integration.
Proficiency with a cloud platform such as AWS, Azure, or GCP.

Nice To Haves

Experience with RAG architectures and vector databases.
Experience with AI evaluation frameworks, guardrails, and monitoring.
Experience with workflow automation tools and enterprise integrations.
Experience working in regulated environments such as life sciences, healthcare, or financial services.
Familiarity with clinical data, CDISC standards, or clinical development workflows.
Experience in high-growth or PE-backed SaaS environments.

Responsibilities

Design and deploy agentic AI workflows and automation solutions across enterprise functions, including R&D, Engineering, Professional Services, IT, Finance, Sales, and Marketing.
Build production-grade multi-agent workflows, RAG-based applications, document intelligence and summarization pipelines, and workflow and process automation solutions.
Rapidly prototype, validate, and deploy solutions, aiming to deliver in weeks rather than months.
Translate business problems into working AI systems, design lightweight architectures, and iterate quickly.
Develop prompt engineering strategies, orchestration logic, API integrations, data pipelines, and testing, validation, and monitoring frameworks.
Partner directly with stakeholders to refine outputs and drive adoption.
Deliver continuous output through weekly or bi-weekly releases.
Prioritize use cases based on ROI and business impact, technical feasibility, and speed to value.
Build and maintain reusable prompt templates, agent design patterns, and integration utilities.
Contribute to lightweight standards for security and data handling, responsible AI practices, and evaluation and performance monitoring.
Provide technical guidance to a small team of AI engineers and automation specialists.
Lead by example through hands-on contribution and support the evolution of the AI engineering function as it scales.
Use company-approved AI tools appropriately, protect confidential information, validate AI-generated content, and exercise sound professional judgment.