[Hiring] AI/ML Engineer @Sparix Global.

Role Description

• Design and contribute to systems that enable rapid ML and GenAI development with high availability and strong observability.
• Build and enhance evaluation frameworks for ML, GenAI, and agentic AI solutions, including relevance, accuracy, cost, latency, and token usage.
• Design and execute LLM behavioral evaluations, including hallucination, grounding, and multi-turn interactions.
• Apply evaluation techniques using golden datasets and LLM-as-judge frameworks to ensure reliable performance.
• Implement multi-trial testing using metrics such as pass@K to assess LLM consistency and reliability.
• Fine-tune LLMs for domain-specific applications, including classification tasks.
• Develop APIs and services to support access to AI solutions and their evaluation pipelines.
• Contribute to ML infrastructure, including deployment, monitoring, and performance optimization.
• Write clean, scalable, and maintainable code, primarily in Python.
• Develop and maintain automated tests (unit, integration, functional) to ensure system reliability.
• Troubleshoot and debug issues related to ML performance, evaluation metrics, and production systems.
• Collaborate with product managers, data teams, and engineers to deliver end-to-end AI solutions.
• Partner with senior engineers to follow best practices and improve development processes.
• Support production systems and participate in on-call rotations as needed.
• Explore and experiment with emerging tools and techniques in ML and GenAI evaluation.

Qualifications

• 5+ years of experience in Machine Learning Engineering or advanced degree in a related field.
• Strong proficiency in Python (primary language).
• Hands-on experience with AWS or similar cloud platforms.
• Experience with ML lifecycle including model deployment, data pipelines, and CI/CD.
• Familiarity with Generative AI systems and LLM-based application development.
• Exposure to ML/LLM evaluation frameworks such as RAGAS, DeepEval, or similar tools.
• Experience building or working with evaluation, monitoring, or observability frameworks for ML/GenAI systems.
• Understanding of metrics-driven evaluation, including relevance, accuracy, latency, and cost optimization.
• Strong foundation in data structures, algorithms, and distributed systems.
• Experience debugging production systems and working in Agile environments.
• Basic knowledge of APIs, web protocols (HTTP), and databases.
• Strong communication skills and ability to collaborate across teams.

Requirements

• Java experience (preferred).
• Experience with Docker and Kubernetes (preferred).
• Awareness of Responsible AI, AI governance, or audit frameworks (e.g., ISO 42001) (preferred).
• MLOps engineering experience with governance exposure (preferred).