Role: Full stack AI engineers
Location: Either Columbia, MD or Lislie, IL
Onsite: 4 days a week
only w2 Need senior candidate
Contract: Long Term
Note From Hirning manager, he wants full stack AI engineers. It just cant be in the skills summary, they have to put how they actually used it. See below:
React/Next.js + TypeScript. A better summary of everything is:
Hands on senior AI Engineer + Google Cloud Platform (vertex)+ Gemini/ADK/LangGraph + React/Next.js/TypeScript (not just "familiar with", personally built)
- Google Cloud Platform (Google Cloud Platform) Experience
- Hands-on experience building and deploying solutions in Google Cloud Platform.
- Azure experience is acceptable in addition to Google Cloud Platform
- LLMs, Generative AI & RAG
- Experience leveraging Gemini, Azure OpenAI, and other Large Language Models.
- Strong hands-on experience implementing Retrieval-Augmented Generation (RAG) solutions.
- Agent Framework Experience
- Direct experience with Google ADK, LangGraph, or similar agentic AI frameworks.
- Building AI agents/bots
- Senior-Level, Hands-On Engineer
- 6+ years of experience (preferably 8+).
- Must be a "working tech lead" type of engineer who owns code and actively develops rather than just leading projects.
Position details
- New project initiative beginning next week. Manager is looking to add an experienced senior-level AI engineer who can immediately contribute to development efforts and eventually grow into a technical leadership role. The position is expected to support a multi-year initiative
This individual will be responsible for building AI-powered applications, bots, and agent-based solutions primarily within Google Cloud Platform environments. The team is developing AI solutions leveraging LLM technologies, RAG architectures, and agent frameworks to support ongoing enterprise AI initiatives. The project is expected to run for several years
- Top Must-Haves:
- Design and develop AI applications and intelligent agents on Google Cloud Platform.
- Develop AI-powered bots and automation solutions.
- Implement and maintain RAG (Retrieval Augmented Generation) solutions.
- Leverage Gemini, Azure OpenAI, and other large language models.
- Build solutions using agent frameworks such as Google ADK and LangGraph.
- Perform hands-on coding and development activities.
- Collaborate with technical leads and architects to deliver AI initiatives.
- Participate in code ownership, architecture discussions, and solution design.
- Grow into a technical leadership role while remaining highly hands-on
Must Have
- Google Cloud Platform (Google Cloud Platform) experience.
- AI/ML engineering experience.
- Google ADK framework experience.
- LangGraph experience.
- Experience leveraging LLMs.
- Gemini experience.
- Azure OpenAI experience.
- RAG implementation experience.
- Python development.
- React or Next.js.
- TypeScript.
- Strong software engineering background.
- Hands-on development experience building AI products.
Nice to Have
- Experience leading development teams.
- Agentic AI development experience.
- Prior experience serving as a Tech Lead.
- Experience architecting enterprise AI solutions.
- Multi-cloud experience with Azure alongside Google Cloud Platform
Experience Required
- 8+ years preferred (manager emphasized a very senior resource).
- Must possess senior-level engineering maturity.
- Candidate may currently be a Senior AI Engineer, Senior Software Engineer, or Working Tech Lead.
- Looking for someone capable of operating as a Tech Lead while remaining hands-on with coding.
What Will Make Someone Successful?
- Extensive hands-on AI development experience.
- Strong Google Cloud Platform expertise.
- Deep knowledge of LLMs, Gemini, Azure OpenAI, and RAG.
- Ability to own code and development efforts independently.
- Comfortable leading technical direction while still contributing code.
- Strong communication and collaboration skills.
- Ability to work effectively within the Covista PMO environment.
Previous Companies / Background Targets
Target candidates from organizations actively building:
- Enterprise AI applications.
- Conversational AI solutions.
- Agentic AI platforms.
- Cloud-native AI products on Google Cloud Platform or Azure.
- LLM-based enterprise solutions. (Suggested based on stated technical requirements.)
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