Senior AI Engineer

Websenor is hiring for Remote

Position:- Senior AI Engineer
Experience:- 5+ years

Location:Remote

---

Job Role:-

The Senior AI Engineer will work on building production-grade AI systems with a strong engineering focus. This role requires someone who can handle end-to-end AI system ownership, from model selection to deployment and scaling, while working on advanced agentic AI systems using LLMs.

---

Responsibilities:-

Infrastructure & Production Engineering

• Design and manage AI infrastructure and pipelines
• Build scalable systems including inference services, data pipelines, and evaluation pipelines
• Work with cloud platforms (AWS/GCP) and contribute to DevOps practices
• Ensure system reliability, scalability, and performance in production

End-to-End AI System Ownership

• Design, build, and maintain production-ready AI systems
• Handle full lifecycle: prototyping → evaluation → deployment → scaling → monitoring
• Ensure robustness and maintainability in real-world applications

Agentic AI System Design

• Architect and implement multi-agent workflows
• Develop prompt strategies, tool integrations, memory systems, and planning mechanisms
• Work on context management, long-horizon reasoning, and tool orchestration

Model Strategy & Optimization

• Select appropriate models based on cost, latency, and performance trade-offs
• Fine-tune or adapt open-source models where required
• Build evaluation frameworks for continuous system improvement

Cross-Functional Collaboration

• Work closely with product, research, and engineering teams
• Mentor junior engineers and contribute to technical direction
• Translate ambiguous requirements into scalable engineering solutions

---

Requirements:-

Core Engineering Skills

• 4+ years of experience in software or AI engineering
• Strong experience in building and deploying production systems
• Proficiency in Python and backend frameworks
• Strong understanding of system design, scalability, and reliability

AI / LLM Experience

• Hands-on experience with LLMs and agent-based systems
• Knowledge of retrieval techniques, fine-tuning, or model adaptation
• Experience in deploying AI models in real-world environments

System Thinking

• Ability to connect model capabilities with system design
• Strong understanding of trade-offs between cost, latency, and quality
• Ability to decide between engineering solutions vs model improvements

---

Good to Have:-

• Experience with multi-agent frameworks (LangGraph, AutoGen, etc.)
• Familiarity with vector databases or retrieval systems
• Experience with distributed systems or large-scale pipelines
• Exposure to scientific or domain-specific applications

---

What We’re Looking For:-

• Strong engineering mindset (not just model usage)
• Ability to handle complex, ambiguous problems
• Comfortable working across system design and implementation levels

---

Salary:- As per industry standards. No bar for the right candidate.