Generative AI Engineer || Remote || Permanent Fulltime
Posted 2026-05-05
Remote, USA
Full-time
Immediate Start
- *Role: AI Engineer
- *Location: Frederick, MD/Remote
- *Permanent Fulltime
- *Job description:
- We are seeking an AI Engineer specializing in Generative AI and Agentic AI systems. This role focuses on designing, developing, and operationalizing intelligent AI agents, Large Language Model (LLM)-based applications, Retrieval-Augmented Generation (RAG) systems, and autonomous multi-agent workflows.
- *Key Responsibilities
- *GenAI Development &
- LLM Engineering
- Build and deploy LLM-based applications leveraging frameworks like LangChain.
- Develop RAG pipelines using vector databases for enterprise knowledge retrieval.
- Develop data pipelines to create structured and unstructured datasets for LLM and agent workflows.
- Optimize prompts, system instructions, and memory architectures for robust, domain-specific reasoning.
- Evaluate model performance—accuracy, hallucination mitigation, latency, and safety compliance.
- *2. Agentic AI Design &
- Autonomous Workflow Engineering
- Implement agentic systems capable of planning, reasoning, tool usage, and multi-step decision-making.
- Build multi-agent ecosystems (task agents, planning agents, critic agents, evaluation agents) to automate complex workflows.
- Integrate agents with APIs, enterprise systems, and external tools to create end-to-end autonomous solutions.
- Ensure agent alignment with Responsible AI principles—traceability, guardrails, human oversight.
- *3. AI Systems Integration &
- Deployment
- Build scalable microservices and APIs for GenAI and agentic components.
- Deploy models and agents using Azure ML or Kubernetes-based stacks.
- 4. Collaboration &
- Influence
- Engage with business and product stakeholders to convert ambiguous use cases into technical solutions.
- Support internal capability building—AI best practices, prompt engineering, GenAI safety, and evaluation frameworks.
- *Required Skills &
- Qualifications**
- Strong hands-on expertise in Python, LLM frameworks, and ML/DL libraries (Transformers, PyTorch, TensorFlow, scikit-learn).
- Experience with API development, microservices, Docker, and Kubernetes.
- Experience building RAG systems with vector databases and embeddings.
- Experience with agentic frameworks or building custom autonomous agents.
- Strong understanding of LLM safety, hallucination mitigation, and evaluation techniques.
- Cloud proficiency in Azure (or any other hyperscalers - AWS, GCP etc)