# Machine Learning Engineer > Weekday AI · Bengaluru, India · Full-time · Posted 2026-07-14 **Workplace:** on_site **Department:** Weekday's Client via platform ## Description **This role is for one of the Weekday's clients** Min Experience: 5+ years Location: Bengaluru JobType: full-time 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. ## Requirements ### **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 ## Apply [Apply at Weekday AI](https://apply.workable.com/weekday-1/j/C319AB6678/apply) --- Powered by [Workable](https://www.workable.com)