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Senior Machine Learning Engineer

Amgen

Hyderabad

3-5 years

1 day ago

$25.3K–38.6K/yr

Full-time

Onsite

Skills Required

LLM

RAG

Embeddings

Vector Database

Gen AI

Agent-based Systems

Prompt Management

MLOps

CI/CD Pipelines

Python

Java

REST API

Microservices

Backend Platform Services

Docker

Description

Seeking a Senior Machine Learning Platform Engineer to design, build, and scale enterprise-grade machine-learning and generative-AI platform capabilities. The role focuses on platform engineering, software engineering, MLOps, and GenAI engineering to enable scalable AI solutions.

Role: Senior Machine Learning Platform Engineer

Experience

3–5 years of experience in machine learning engineering, ML platform engineering, MLOps, backend engineering, cloud engineering or enterprise AI systems

Responsibilities

Design and build reusable ML and GenAI platform capabilities supporting model development, experimentation, evaluation, deployment and production operations
Build self-service platform services, APIs and automation to abstract infrastructure complexity
Develop and maintain MLOps capabilities including experiment tracking, model and prompt registries, evaluation frameworks, deployment workflows and automated promotion
Build model-serving and inference capabilities for classical ML models, deep-learning models and LLMs via scalable REST, gRPC or event-driven interfaces
Develop platform capabilities for GenAI and agentic systems including model access, prompt management, embeddings, vector search, Retrieval-Augmented Generation, tool integration and agent frameworks
Engineer integrations with cloud-based AI and data platforms using APIs, SDKs and managed services
Build and maintain containerized platform services using Docker and Kubernetes including deployment patterns, scaling strategies, service configuration and lifecycle management
Design and implement platform APIs, SDKs, templates and shared libraries to establish standardized development patterns
Implement observability and operational monitoring including logs, metrics, distributed tracing, service health, model/LLM usage, latency, errors and dashboards
Implement AI evaluation and quality-management capabilities including automated evaluation pipelines, regression testing, model comparison and release-quality gates
Build security and governance controls into platform capabilities including authentication, authorization, secrets management, data access controls, auditability, lineage and responsible-AI controls
Design platform mechanisms for usage metering, cost visibility and optimization
Engineer platform services for scalability, reliability and resilience including retries, asynchronous processing, concurrency controls, fault tolerance and graceful failure handling
Develop and improve CI/CD pipelines for AI platform services, reusable components and model-based workloads including automated testing, artifact management and environment promotion
Evaluate new AI, ML and cloud technologies for shared platform capabilities versus application-specific solutions
Partner with data scientists and ML engineers to identify recurring challenges and convert them into reusable platform patterns and services
Provide technical guidance on architecture, scalability, performance, security and production-readiness for ML and GenAI workloads
Participate in architecture reviews, code reviews, incident resolution and production troubleshooting across application, platform and infrastructure layers
Create and maintain technical designs, architecture decision records, development standards, operational runbooks and platform documentation
Help define the longer-term technical roadmap and engineering standards for ML and GenAI platform capabilities

Nice To Have

Experience with Java or another enterprise programming language

More Skills

machine learning engineering, ML platform engineering, backend engineering, cloud engineering, enterprise AI systems, software-engineering fundamentals, Kubernetes, containerization, orchestration technologies, MLOps concepts, GenAIOps concepts, experiment tracking, model lifecycle management, evaluation, deployment, monitoring, reproducibility, MLflow, Kubeflow, SageMaker, Databricks, model-serving infrastructure, GenAI architecture patterns, LLM APIs

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Senior Machine Learning Engineer

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