Roles & Responsibilities
- We are looking for a Senior AI Engineer with strong hands-on experience in designing, building, deploying, and operating AI systems in production. The ideal candidate should have built AI/ML or GenAI solutions from the ground up, including architecture, model development, data pipelines, APIs, deployment, monitoring, and continuous improvement. This role requires someone who can work across the full AI lifecycle — from problem discovery and solution design to production-grade implementation — while collaborating closely with business users, data teams, engineering teams, and platform stakeholders.
- Key Responsibilities
- Design and build end-to-end AI, Machine Learning, and Generative AI solutions from scratch.
- Translate business problems into scalable AI system designs and technical implementation plans.
- Develop and deploy production-ready AI applications, including APIs, backend services, workflows, and user-facing AI features.
- Build and maintain data pipelines required for model training, inference, evaluation, and monitoring.
- Implement LLM-based solutions using RAG, prompt engineering, agentic workflows, embeddings, vector databases, and orchestration frameworks.
- Evaluate, fine-tune, and optimize models for accuracy, latency, cost, reliability, and business impact.
- Design secure and governed AI systems with proper access control, data privacy, auditability, and responsible AI practices.
- Deploy AI workloads on cloud or containerized platforms using CI/CD, Docker, Kubernetes, and MLOps/LLMOps practices.
- Monitor production AI systems for performance drift, hallucination risks, failures, cost anomalies, and user adoption.
- Collaborate with product owners, data engineers, software engineers, architects, and business teams to deliver measurable outcomes.
- Create technical documentation, architecture diagrams, deployment guides, and operational runbooks.
- Mentor junior engineers and support best practices in AI engineering, code quality, testing, and production operations.
Education & Qualifications
- Bachelor’s or master’s degree in computer science, Data Science, Artificial Intelligence, Machine Learning, Engineering, Mathematics, or a related discipline.
- Equivalent hands-on industry experience will also be considered.
Ideal Candidate
The ideal candidate is a hands-on builder who has successfully designed and delivered AI systems in production, not just prototypes or proof-of-concepts. They should be comfortable working across architecture, engineering, data, models, deployment, monitoring, and business stakeholder engagement. They should be able to take ownership of complex AI initiatives, make practical technology choices, and deliver reliable AI solutions that solve real business problems at scale.