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 .

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