AI Engineer

Agentic AI & LLM Engineering

• Design and build agentic AI systems and multi-agent frameworks that automate complex, multi-step workflows for enterprise customers.
• Develop and deploy LLM-powered applications using techniques including RAG, fine-tuning, prompt engineering, function calling, and tool use.
• Build production-ready applications independently: backend services, frontend interfaces, and the AI/ML layer connecting them
• Implement guardrails, evaluation frameworks, and responsible AI controls to ensure production-grade reliability and safety.
• Stay current with the rapidly evolving agentic AI landscape - MCP, LLM orchestration frameworks, reasoning models - and bring the best of it into customer engagements.

Predictive AI & Machine Learning

• Rapidly build, iterate, and deploy multiple predictive ML models (classification, regression, time-series forecasting, anomaly detection) for enterprise customers
• Architect hybrid AI solutions that combine predictive ML (scoring, forecasting, classification) with Generative AI (natural language interfaces, RAG-based explanations, agentic orchestration) 

End-to-End AI Application Development

• Own the full development lifecycle: from problem framing and data exploration through model development, API integration, and production deployment.
• Build scalable backend services and APIs that expose AI capabilities to enterprise applications and workflows.
• Integrate AI models into customer environments - cloud, on-prem, and hybrid - ensuring performance, stability, and maintainability at scale.
• Develop ML pipelines (for both predictive models and LLM applications) and MLOps/LLMOps infrastructure that support continuous model improvement, automated retraining, drift detection, and monitoring in production.

Customer Engagement & Delivery

• Work directly with customer data scientists, engineers, and business stakeholders to translate real-world problems into AI solutions.
• Execute with a high degree of autonomy: taking full ownership to architect, prioritize, and ship production-grade solutions with minimal oversight.
• Contribute to pre-sales and proof-of-concept engagements - building fast, credible demonstrations that win technical trust.
• Communicate clearly across audiences: from detailed technical design reviews with engineering teams to outcome-focused updates for business stakeholders.
• Collaborate closely with Program Managers, Solution Engineers, and Kaggle Grandmasters to deliver cohesive, high-quality solutions.

What We Are Looking For

Experience & Background

• 4+ years of hands-on AI/ML engineering experience, including end-to-end model development and production deployment.
• Demonstrable experience building LLM-powered applications - RAG pipelines, agentic workflows, fine-tuned models, or similar.
• Proven track record of rapidly building and deploying multiple predictive ML models — including classification, regression, time-series forecasting, or anomaly detection on structured/tabular enterprise data.
• Strong Python engineering skills; experience with ML frameworks (PyTorch, TensorFlow, scikit-learn) asnd LLM tooling (LangChain, LlamaIndex, or equivalent).
• Experience deploying models and AI services in cloud or enterprise environments (AWS, Azure, GCP, on-prem Kubernetes).

Skills & Capabilities

• Deep understanding of modern GenAI concepts: prompt engineering, RAG, fine-tuning, RLHF, model evaluation, guardrails, and LLMOps.
• Solid grounding in classical ML - able to select the right tool for the problem, not just default to the latest LLM.
• Backend development skills: REST APIs, containerization (Docker/Kubernetes), and CI/CD pipelines for AI applications.
• Strong problem-solving instincts - comfortable with ambiguity, able to move fast without sacrificing engineering quality.
  • Clear communicator who can explain complex AI systems to non-technical stakeholders without oversimplifying.