AI Engineer

VAS is seeking an AI Engineer to lead building of scalable real-time production grade applications that use AI/ML models. This is a strategic, hands-on position for an experienced technical leader who has a track record of shipping AI-enhanced customer applications and tooling used by engineering teams.

This role will be focused on embedding LLMs, agent-based systems, and automation into core development workflows—boosting productivity, reducing manual toil, and accelerating delivery, essentially transforming how our engineers build, test, and ship software.

Responsibilities

• Understand customer challenges and how integrating AI capabilities can help lead to solutions that have AI as a differentiator. 
• Identify opportunities to apply AI for efficiency, growth, and customer value.
• Drive awareness of AI capabilities and demonstrate how it can address customer needs, improve efficiency, reduce costs, and drive growth.
• Drive transformation from AI-Ad Hoc to AI-Native engineering practices.
• Serve as the AI technical SME, conduct R&D (research and development) to meet the needs of our AI strategy.
• Continuously assess emerging AI tools and make data-driven recommendations.
• Measure & Accelerate Adoption: Establish KPIs, track progress from the current to 100% adoption, implement interventions to accelerate uptake and communicate impact.
• Build Center of Excellence: Create forums for knowledge sharing, celebrate wins, and foster peer-to-peer learning.
• Establish AI governance frameworks and guardrails covering compliance, security, privacy, and ethical AI practices, and embed them into development workflows.
• LLM Agents & Prompt Engineering
  • Architect and implement LLM agents.
  • Build composable, tool-augmented reasoning chains (e.g., RAG, CoT, ReAct, planner-executor).
  • Integrate vector databases and knowledge graphs to support retrieval-augmented generation (RAG).
  • Design and maintain high-quality prompt strategies for robustness and reliability.
• Model Context Protocol (MCP) & Backend
  • Develop and maintain scalable APIs, supporting synchronous and asynchronous agent execution.
  • Integrate Model Context Protocol (MCP) to enable secure and structured access to external data and tools within agent workflows.
  • Implement state tracking, context-aware input dispatch, and modular plugin integration within the control plane.
• Evaluation, Testing & Observability
  • Build unit and behavioral tests for agents, tools, and workflows.
  • Develop tooling for trace analysis, agent state debugging, and hallucination tracking.
  • Compare and benchmark agent orchestration frameworks for trade-offs in speed, reliability, and usability.
• Model Fine-Tuning & MLOps
  • Integrate, deploy, fine tune and monitor models in production using cloud providers.
  • Set up agent logging, observability dashboards, and recovery workflows.
• Front-end & User Experience
  • Collaborate with front-end developers or build user-facing components using React, TypeScript.
  • Ensure seamless user and agent interaction via UI and API bridges.

Education & Experience Requirements

• Bachelor of Science in Software Engineering, Computer Science, Data Science, AI/ML or related field preferred.
• 10+ years of experience in software development and design.
• Proven experience in AI/ML solution design and hands experience with AI-powered development tools. 3+ years of experience preferred. 
• Strong knowledge of Large Language Models, Generative AI, NLP, and Machine Learning concepts. (3+ years of experience preferred).
• Hands-on experience with deep learning frameworks. 
• Experience with RAG pipelines, vector databases, and agentic frameworks.
• Familiarity with cloud-based AI services.