GenAI Engineer

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About DATAECONOMY:
We are a fast‑growing data & analytics company headquartered in Dublin with offices in Dublin, OH, Providence, RI, and an advanced technology center in Hyderabad, India. We are clearly differentiated in the data & analytics space via our suite of solutions, accelerators, frameworks, and thought leadership.

We are looking for a skilled AI Engineer with proven experience developing and deploying large language models (LLMs) and generative AI systems. In this role, you’ll design, fine‑tune, and operationalize models from leading providers such as OpenAI, Llama, Gemini, and Claude, along with leveraging open‑source models from platforms like Hugging Face. You’ll also build robust multi‑step workflows and intelligent agents using frameworks such as Microsoft Auto Gen, Lang Graph, and CrewAI.

This position requires strong technical expertise in generative AI, advanced software engineering abilities, fluency in Python (including FastAPI), and a solid understanding of MLOps/LLMOps principles.

• LLM Solution Design & Implementation:
  Architect, develop, and implement LLM‑powered and generative AI solutions using proprietary and open‑source technologies (e.g., GPT‑4, Llama 3, Gemini, Claude). Customize and fine‑tune models for tasks such as chatbots, summarization, and content classification.
• Prompt Engineering & Model Tuning:
  Craft, refine, and test model prompts to achieve targeted outputs. Fine‑tune pre‑trained LLMs using custom data and advanced techniques such as instruction tuning or reinforcement learning with human feedback.
• Agentic Frameworks & Workflow Automation:
  Build and maintain stateful, multi‑agent workflows and autonomous AI agents using frameworks like Microsoft Auto Gen, Lang Graph, Lang Chain, Llama Index, and CrewAI. Develop custom tools for API integration and task orchestration.
• Retrieval‑Augmented Generation (RAG):
  Design and deploy RAG pipelines by integrating vector databases (e.g., Pinecone, Faiss, Weaviate) for efficient knowledge retrieval. Use tools like RAGAS to ensure high‑quality, traceable response generation.
• LLM API Integration & Deployment:
  Serve LLMs via FastAPI‑based endpoints and manage deployment using Docker containers and orchestration tools such as Kubernetes and cloud functions. Implement robust CI/CD pipelines for scalable, reliable, and cost‑efficient production environments.
• Data Engineering & Evaluation:
  Construct data pipelines for ingestion, preprocessing, and controlled versioning of training datasets. Set up automated evaluation systems, including A/B tests and human‑in‑the‑loop feedback, to drive rapid iteration and improvement.
• Team
  
  Collaboration:
  
  Partner with data scientists, software engineers, and product teams to scope and integrate generative AI initiatives. Communicate complex ideas effectively to both technical and non‑technical stakeholders.
• Monitoring, LLMOps, & Ethics:
  Deploy rigorous monitoring and observability tools to track LLM usage, performance, cost, and hallucination rates. Enforce LLMOps best practices in model management, reproducibility, explainability, and compliance with privacy and security standards.
• Continuous Learning & Thought Leadership:
  Stay abreast of the latest developments in AI/LLMs and open‑source innovations. Contribute to internal knowledge sharing, champion new approaches, and represent the organization at industry or academic events.

• Open‑Source & Community:
  Participation in open‑source AI/ML projects or a strong Git Hub profile showcasing relevant contributions or publications.
• Multi‑Agent Systems:
  Hands‑on experience with advanced agentic frameworks or autonomous agent system design.
• Data Governance & Compliance:
  Knowledge of data governance, security protocols, and compliance standards.
• Search & Databases:
  Deep expertise in vector similarity search, indexing, and familiarity with document stores (e.g., Mongo
  
  DB, Postgre
  
  SQL) as well as graph databases.
• Cloud‑Native AI Services:
  Experience with cloud‑native AI services like Azure ML, Cognitive Search, or equivalent platforms for scalable generative AI…