Senior Machine Learning Engineer

Overview

We are seeking a Senior AI/ML Engineer (MLOps) to design, deploy, monitor, and continuously improve large-scale AI and machine learning solutions in production. This role focuses on ensuring the reliability, performance, governance, and operational excellence of both Generative AI and traditional ML systems , while collaborating closely with data scientists, engineers, platform teams, and business stakeholders.

Key Responsibilities

• Design and implement monitoring, alerting, and observability solutions for ML and AI systems, including model performance, data drift, latency, and AI operational health.
• Build and maintain automated evaluation, testing, and validation pipelines for ML models, GenAI workflows, prompts, and agentic systems.
• Investigate and resolve production issues related to model behavior, AI applications, data quality, integrations, and RAG/retrieval pipelines.
• Manage model, prompt, embedding, and vector index lifecycles, including versioning, rollout strategies, and evaluation gates.
• Collaborate with platform and infrastructure teams to optimize AI deployments for scalability, reliability, and cost efficiency.
• Establish feedback loops using user interactions, business metrics, UAT findings, and expert reviews to drive continuous improvement.

General Background

• Bachelor's degree in Computer Science, Data Science, Engineering, Information Technology, or a related field.
• 5+ years of experience in Machine Learning, AI Engineering, MLOps , or related software engineering roles.
• Experience supporting production AI/ML systems in cloud environments and agile delivery teams.

Mandatory Skills

• Strong Python expertise including Pandas, NumPy, and Scikit-learn for model maintenance, automation, and production support.
• Hands-on experience building or supporting production Generative AI solutions , including RAG, AI agents, LlamaIndex, CrewAI, or similar orchestration frameworks.
• Strong understanding of Machine Learning fundamentals, model evaluation techniques, drift detection, bias monitoring, and model performance management.
• Proven experience in MLOps, orchestration, and model monitoring , using tools such as Kubernetes , Docker, Prometheus, Grafana, or cloud-native monitoring platforms to track model performance, data quality, latency, and operational metrics.
• Production experience on at least one major cloud provider (Azure, AWS, GCP).
• Practical experience working in Agile environments (Scrum/Kanban).

Nice-to-Have Skills

• Experience with Apache Spark, Dask, or other distributed data processing frameworks.
• Familiarity with model serving and deployment platforms such as Azure ML Inference, Triton, SageMaker Endpoints, and API frameworks like FastAPI or Flask.
• Experience with orchestration and ML lifecycle tools such as Airflow, Kubeflow, or MLflow.
• Exposure to Infrastructure as Code (IaC) tools such as Terraform.
• Experience mentoring engineers and driving engineering best practices across teams.
• Experience working with Microsoft Azure AI/ML ecosystem, including services such as Azure Machine Learning, Databricks, or related cloud-based AI deployment platforms.