AI Deployment Manager

We are looking for a hands-on AI Engineer who can build, deploy, run, and debug AI/ML solutions in production. The ideal candidate should be comfortable taking AI solutions from development through deployment, monitoring, debugging optimization. This is a hands-on engineering role and requires strong coding and problem-solving skills.

Responsibilities:

• Design, develop, and deploy AI/ML and Generative AI solutions for real-world use cases.
• Write clean, production-ready code using Python and relevant AI/ML frameworks.
• Deploy AI/ML models, LLM applications, agents, APIs, and automation workflows into production environments.
• Take ownership of the complete lifecycle of AI solutions, from development to production deployment.
• Run and test applications in production, monitor performance, and identify potential issues.
• Debug issues across code, models, APIs, data pipelines, infrastructure, and integrations.
• Troubleshoot production failures and drive issues to resolution.
• Work with logs, monitoring tools, APIs, databases, and cloud environments to identify root causes.
• Optimise AI applications for performance, latency, reliability, scalability, and cost.
• Build and maintain automated workflows, AI agents, RAG pipelines, and LLM-based applications.
• Integrate AI solutions with existing products, APIs, databases, and internal systems.
• Work closely with Product, Engineering, and Data Science teams to understand requirements and deliver solutions.
• Continuously improve deployed AI systems based on production feedback and performance metrics.
• Write scripts and automation to reduce manual operational work.

Requirements:

• 2-6 years of experience in AI/ML Engineering, GenAI, or Applied AI.
• Strong hands-on experience with Python.
• Experience building and deploying AI/ML or GenAI applications in production.
• Strong understanding of LLMs, RAG, AI Agents, prompt engineering, or ML models.
• Experience with REST APIs and integrating third-party/internal services.
• Good debugging and troubleshooting skills.
• Experience working with Git, Docker, Linux/Unix, and cloud environments.
• Good understanding of databases and SQL.
• Experience with monitoring, logging, testing, and production issue resolution.
• Ability to understand existing code, modify it, run it, and debug issues independently.

Good to Have:

• Experience with AWS / GCP / Azure.
• Experience with LangChain, LangGraph, OpenAI APIs, Hugging Face or similar frameworks.
• Experience with vector databases such as Pinecone, Weaviate, Milvus, or PGVector.
• Experience with CI/CD and deployment pipelines.
• Kubernetes and container orchestration experience.
• Experience with LLM evaluation, observability, and performance optimisation.

Ideal Candidate:

The ideal candidate is someone who can say:

• I built it, deployed it, ran it in production, monitored it, and when something broke, I debugged and fixed it.
• We are not looking for someone who has only worked on AI/ML POCs or research projects.
• Strong hands-on coding, deployment, debugging, and production ownership are essential.