Sr. AI/ML Engineer

Job Summary

We are seeking a Senior AI/ML Engineer to design, deploy, operate, and optimize production-grade AI and Machine Learning solutions at scale. This role focuses on LLM/Generative AI applications, MLOps, cloud platforms, and software engineering excellence , ensuring AI systems are reliable, observable, cost-efficient, secure, and production-ready.

You will work closely with data scientists, platform engineers, and product teams to build and support RAG, agentic AI, and ML solutions across the full development lifecycle.

Key Responsibilities

Design, build, deploy, and support production AI/ML applications, including LLM-powered, RAG, and agent-based solutions. Develop robust evaluation, testing, observability, and monitoring frameworks for AI systems. Implement and maintain CI/CD pipelines for ML and GenAI workloads. Monitor and optimize model performance, latency, reliability, cost, and operational health.

Build and manage AI infrastructure using Infrastructure-as-Code and cloud-native services. Troubleshoot production issues across models, data pipelines, retrieval systems, agents, and integrations. Collaborate with engineering, data science, and platform teams to deliver scalable AI solutions. Drive engineering best practices including code reviews, testing, version control, and documentation.

Implement governance, guardrails, tracing, logging, and monitoring to ensure responsible AI deployment. Mentor junior engineers and contribute to technical leadership within the team. General Qualifications Bachelor's degree in Computer Science, Software Engineering, Data Science, Artificial Intelligence, or a related discipline.

5+ years of experience in Software Engineering, Machine Learning Engineering, MLOps, or AI Engineering roles. Experience designing and supporting production systems in cloud environments. Strong communication, stakeholder management, problem-solving, and mentoring capabilities. Mandatory Requirements Strong Python programming expertise with experience building and maintaining production-grade applications.

Solid software engineering fundamentals , including testing, code reviews, Git/version control, and maintainable code practices. Proven experience delivering and supporting LLM/Generative AI applications in production . Hands-on experience with RAG architectures, AI agents, and/or fine-tuned LLMs . Strong understanding of LLM evaluation, guardrails, observability, latency optimization, and cost management .

Experience implementing Infrastructure-as-Code using Terraform or equivalent IaC tools. Production experience on at least one major cloud platform ( Azure, AWS, or GCP ). Experience with Databricks or comparable lakehouse/MLOps platforms. Hands-on experience with Docker and Kubernetes for containerized AI/ML workloads.

Experience building and supporting CI/CD pipelines for ML and AI deployments . Strong knowledge of LLM tracing, logging, telemetry, and observability frameworks . Experience implementing monitoring solutions using tools such as Prometheus, Grafana, OpenTelemetry, Datadog, CloudWatch, or Azure Monitor . Nice-to-Have Skills Experience with TensorRT-LLM , FlashAttention , or other LLM inference optimization technologies.

Knowledge of tensor parallelism and pipeline parallelism for large-scale model deployment. Experience with AI orchestration frameworks such as LangGraph, LlamaIndex, AutoGen, or Semantic Kernel . Familiarity with Model Context Protocol (MCP) . Experience with LLMOps tooling , including LiteLLM, model routing/fallback strategies, prompt/version management, and token cost monitoring.

Experience with workflow orchestration platforms such as Airflow, Dagster, Kubeflow, or Argo . Relevant cloud, AI, Kubernetes, or Databricks certifications . Previous experience in consulting, professional services, or client-facing delivery environments .