Machine Learning Engineer

We are looking for a Machine Learning Engineer to build and operate the production infrastructure that transforms machine learning research into scalable, reliable, and low-latency AI services. You will partner closely with Applied Science, Product, and Platform Engineering teams to operationalize machine learning models, LLM-powered applications, and agentic workflows that power real-world enterprise products.

This role focuses on building production-ready ML systems, developing MLOps infrastructure, and ensuring AI services are secure, observable, cost-efficient, and highly available. You'll play a key role in enabling both traditional machine learning models and modern generative AI applications to move seamlessly from experimentation into production.

Key Responsibilities

Production Machine Learning Systems

• Convert prototype machine learning models into production-grade, scalable services with well-defined API interfaces.


• Deploy and optimize models across various domains including predictive analytics, recommendation systems, forecasting, NLP, and generative AI.


• Refactor, containerize, version, deploy, and continuously monitor machine learning models for production readiness.


• Collaborate with Applied Science teams to improve model performance, scalability, and operational efficiency.

LLM & Agentic AI Infrastructure

• Integrate AI applications with enterprise LLM gateways, model routing systems, and prompt management frameworks.


• Support retrieval-augmented generation (RAG), vector search, and knowledge retrieval architectures.


• Build and maintain agentic AI workflows, orchestration frameworks, and safe execution patterns.


• Implement prompt versioning, experimentation, A/B testing, dynamic orchestration, and AI safety guardrails.

MLOps & Platform Engineering

• Design and maintain CI/CD pipelines for machine learning models and AI services.


• Build batch and streaming data pipelines using modern orchestration and distributed processing frameworks.


• Develop online feature pipelines, feature stores, model registries, and experiment tracking infrastructure.


• Automate model lifecycle management, deployment workflows, rollback strategies, and continuous delivery.

Microservices & Distributed Systems

• Develop high-performance inference services using REST and gRPC APIs.


• Build scalable microservices supporting low-latency online predictions.


• Implement schema versioning, structured outputs, and API reliability standards.


• Optimize service performance to consistently meet stringent latency and availability targets.

Monitoring, Reliability & Observability

• Implement comprehensive monitoring across AI systems, including traces, logs, metrics, model performance, and infrastructure health.


• Detect model drift, data quality issues, feature degradation, and operational anomalies.


• Design resilient systems with autoscaling, caching, retries, circuit breakers, fallback mechanisms, and graceful degradation.


• Track infrastructure utilization, latency, cost, and AI service quality through production dashboards.

Developer Experience & Enablement

• Create reusable SDKs, templates, command-line tools, and deployment frameworks.


• Build sandbox environments and documentation that simplify AI application development.


• Collaborate with engineering teams to establish best practices for production ML, MLOps, and AI engineering.


• Mentor engineers and contribute to improving platform standards and development workflows.

Required Qualifications

• 5–11+ years of experience in Machine Learning Engineering, MLOps, Platform Engineering, or Backend Engineering supporting production ML systems.


• Strong software engineering skills with expertise in Python and at least one of Java, Go, or Scala.


• Solid understanding of distributed systems, concurrency, API design, testing, and scalable software architecture.


• Experience deploying and operating production machine learning services.


• Hands-on experience with orchestration frameworks and LLM tooling such as LangChain, LlamaIndex, OpenAI Function Calling, Agent frameworks, or similar technologies.


• Knowledge of retrieval-augmented generation (RAG), vector databases, knowledge graphs, and AI agent architectures.


• Experience building data pipelines using Airflow, Kubeflow, Spark, Flink, Kafka, or similar technologies.


• Strong experience with Docker, Kubernetes, microservices, REST APIs, and gRPC services.


• Familiarity with experiment tracking, model registries, feature stores, drift detection, A/B testing, and shadow deployments.


• Experience implementing observability using tools such as OpenTelemetry, Prometheus, Grafana, or similar monitoring platforms.


• Experience deploying cloud-native applications on AWS or comparable cloud environments.


• Understanding of security best practices including RBAC, secrets management, audit logging, and PII protection.

Preferred Qualifications

• Experience building enterprise AI platforms or large-scale MLOps infrastructure.


• Knowledge of vector databases, retrieval systems, and knowledge graph technologies.


• Experience supporting LLM-powered applications, AI agents, and autonomous workflows.


• Familiarity with cloud cost optimization and multi-tenant SaaS architectures.


• Strong understanding of production reliability engineering and distributed system design.

Ideal Candidate Profile

The ideal candidate:

• Thinks beyond models and focuses on delivering measurable business outcomes.


• Prioritizes reliability, scalability, security, and operational excellence.


• Enjoys designing production systems that balance performance, cost, and maintainability.


• Works effectively across Applied Science, Product, and Engineering teams.


• Believes in automation, developer productivity, and platform engineering best practices.


• Documents processes clearly and enjoys mentoring other engineers.

Why Join Us?

Join a team building next-generation AI infrastructure that enables enterprise-scale machine learning, LLM-powered applications, and intelligent automation. You'll help shape production AI platforms that power real-world products while working with modern MLOps technologies, distributed systems, and cutting-edge generative AI.

Must-Have Skills

• Machine Learning Engineering


• Python


• MLOps


• Kubernetes


• Docker


• REST APIs


• Distributed Systems


• CI/CD


• LLM Applications

Good-to-Have Skills

• Machine Learning


• Python


• LangChain


• LlamaIndex


• Kafka


• Spark


• Airflow


• Kubeflow


• MLflow


• Vector Databases


• Retrieval-Augmented Generation (RAG)


• AWS