Sr AI Engineer

• Build production-grade AI/ML applications and services using Python, following software engineering best practices for clean code, testing, documentation, reliability, and maintainability.
• Design and develop scalable data and ML pipelines for batch and real-time processing using Kafka, distributed processing frameworks, and workflow orchestration tools.
• Implement end-to-end model training, evaluation, deployment, and inference workflows that can scale across large datasets and enterprise workloads.
• Build and deploy REST APIs, microservices, and event-driven integrations to expose AI/ML capabilities to downstream applications and business workflows.
• Work with SQL and NoSQL databases to store, retrieve, transform, and manage structured and unstructured data for AI/ML use cases.
• Support production deployment and lifecycle management through CI/CD, containerization, model versioning, automated validation, and release processes.
• Implement observability and reliability mechanisms, including logging, monitoring, alerting, error handling, and root-cause analysis for production AI systems.
• Optimize model services for latency, throughput, cost, scalability, and operational performance.
• Collaborate with Data Scientists to convert prototypes, notebooks, and experimental models into reliable, maintainable, and scalable production solutions.
• Evaluate and integrate GenAI / LLM components, including prompt workflows, RAG pipelines, evaluators, guardrails, and orchestration patterns where applicable.

About You:

• Bachelor’s degree in Computer Science or equivalent experience, with 4+ years in software design, development, and algorithmic solutions.

Must-Have Skills:

• Proven experience building and deploying end-to-end AI/ML pipelines, including:
  • Data preparation and feature engineering
  • Model training and evaluation
  • Model deployment and inferencing
  • Production monitoring and lifecycle management
• Strong hands-on experience with MLOps practices and tools, including CI/CD for ML, model versioning, automated retraining, and production deployment.

Preferred / Good-to-Have Skills:

• Experience building applications using Generative AI (LLMs), including prompt engineering, RAG architectures, evaluation frameworks, and model orchestration.
• Exposure to Agentic AI systems, including multi-agent workflows, planning, tool usage, orchestration frameworks, and autonomous decision-making patterns.
• Experience implementing LLM observability, evaluation, and guardrails for production GenAI systems.
• Experience building reusable AI platforms or shared ML services used across multiple teams.
• Experience designing and operating scalable inference systems capable of supporting production workloads.
• Good understanding of observability and reliability for ML systems, including monitoring, alerting, performance tracking, debugging, and root-cause analysis.
• Strong software engineering fundamentals, including Python development, testing, code reviews, error handling, and production-quality coding practices.
  • Experience working with cloud-based ML platforms and modern ML frameworks.