• Innovation: We embrace new technologies and encourage experimentation to solve complex business problems.
• Ownership: Every team member is empowered to take initiative and drive projects from concept to production.
• Transparency: We foster open communication, regular knowledge sharing, and inclusive decision-making.
• Continuous Learning: We support professional development through certifications, tech talks, and hands-on learning.
• Impact: Our work directly influences enterprise-wide initiatives, from customer experience to operational efficiency.

Youll be joining a team that believes inbuilding with purpose, where engineering excellence meets data-driven strategy.

Key Responsibilities

• Design and deliver LLM-powered applications such as:
  • Retrieval-Augmented Generation (RAG) with enterprise knowledge sources
  • Agentic workflows (tool/function calling, orchestration, multi-step reasoning)
  • Text extraction/summarization, classification, Q&A, and document intelligence
• Build and optimize retrieval pipelines: chunking strategies, embeddings, vector search, reranking, grounding, and evaluation.
• Create robust evaluation frameworks: golden datasets, automated scoring, offline/online A/B testing, and continuous regression testing for prompts/models.

Required Qualifications

• Bachelors degree in Computer Science, Engineering, or related field (or equivalent experience).
• 6+ years experience in software engineering, ML engineering, or applied AI roles.
• Strong programming expertise in Python, solid SQL, and strong software engineering fundamentals (APIs, testing, CI/CD, code quality).
• Demonstrated experience building and deploying AI/ML systems into production with measurable outcomes.
• Hands-on experience building LLM applications (RAG/agents/tool calling) and implementing evaluation/monitoring practices.
• Experience with vector databases search (e.g., Azure AI Search or equivalent), rerankers, and embedding lifecycle management.
• Familiarity with Responsible AI practices: privacy, security reviews, model risk management, bias/fairness, and explainability.

Core Technical Skills

• LLM Apps: RAG, agents, tool/function calling, prompt engineering, evaluation harnesses, guardrails
• Languages: Python (required), SQL (required)
• Cloud: Azure (deployments, security, observability, CI/CD)
• Ops: Monitoring, incident response, SLO/SLA management, cost optimization
• Data: ETL/ELT, distributed processing concepts, data quality frameworks

Minimum Qualifications

Doctorate (Academic) Degree and 0 years related work experience; Master's Level Degree and related work experience of 3 years; Bachelor's Level Degree and related work experience of 5 years

Swing Shift - 2-11pm