GenAI Engineer

About Catalytics Datum

Catalytics Datum is the Next-Gen Enterprise that amalgamates Data Science, Big Data, Cloud Computing & Business Intelligence to solve complex business problems for enterprises through user experience and faster decision-making. Recognized by Microsoft BizSpark, Catalytics is present across the globe to become your partner in Digital Transformation.

Catalytics Datum offers Platform as a Service, which is One Stop Solution. The complete process; starting from Requirement Gathering to the Final Deployment, is data-driven, processed by collaborative and different Predictive modeling tools which leave clients overhead free. We provide up to 99.9% accurate results in order to increase profitability by providing the deepest insights of your brands.

Role Summary

We are hiring a GenAI Engineer (23 years experience) to build, test, and deploy LLM-powered applications in production. This role requires not just strong engineering skills, but also the ability to work with product and business teams to shape the right GenAI use cases, set realistic expectations, and communicate risks clearly. Youll work on copilots, knowledge assistants, and workflow automation with focus on RAG pipelines, reliability, evaluation, and cost control.

Key Responsibilities

• Build LLM-powered APIs and services for enterprise use cases
• Translate business problems into GenAI workflows (what to automate vs what needs human-in-the-loop)
• Implement RAG pipelines with retrieval, grounding, and evaluation
• Communicate model limitations, risks, and trade-offs to product and stakeholders
• Partner with security/legal teams to ensure responsible GenAI usage
• Participate in design reviews, demos, and stakeholder walkthroughs
• Contribute to internal GenAI playbooks, templates, and best practices

Minimum Qualifications

• 23 years of hands-on experience building GenAI / LLM-based applications
• Strong Python + API development experience
• Practical exposure to RAG, embeddings, and prompt engineering
• Experience deploying GenAI workloads on at least one cloud (Azure/AWS/GCP)
• Ability to explain GenAI workflows and limitations to non-technical stakeholders

What Success Looks Like (First 6 Months)

• Stakeholders understand what the system can and cannot do (low surprise factor)
• Stable RAG pipeline with improved answer quality and controlled hallucinations
• Clear documentation and demo assets created for internal teams