Sr Machine Learning Engineer

Biopharma Careers; Hyderabad; On-site; INR 350,000 - 600,000; Full time

Job summary

Amgen seeks a Sr Machine Learning Engineer to lead end-to-end ML pipelines and platform development for scalable AI solutions. You will act as a player-coach, shaping standards, partnering with DevOps, Security, Compliance and Product teams to deliver a secure, enterprise-grade AI developer experience.

You will design core services, infrastructure and governance for hundreds of practitioners to prototype, deploy and monitor models, including LLMs, while ensuring cost efficiency and robust

Qualifications

• 3-5 years in AI/ML and enterprise software.
• Proficiency in ML algorithms, transformers and GenAI tooling.
• Experience building scalable ML services and AI SaaS offerings.

Responsibilities

• Engineer end-to-end ML pipelines, data ingestion, feature engineering and model training.
• Package models as micro-services using Docker/Kubernetes and expose secure APIs.
• Build full-stack AI apps by integrating model services with UI or workflow layers.
• Optimize performance and cost, tune GPU/CPU autoscaling and SLAs.
• Instrument observability, metrics, tracing and drift detection for live models.
• Embed security and responsible-AI controls with Security and Compliance teams.
• Contribute reusable platform components and promote engineering velocity.

Skills

ML pipelines

Python

Docker/Kubernetes

MLOps

Cloud platforms

Explainability

Tools

Kubeflow

SageMaker Pipelines

OpenAI SDK

Docker

Kubernetes

REST/gRPC

Job description

Career Category

Engineering

Job Description

We are seeking a Sr Machine Learning Engineer, Amgen's most senior individual-contributor authority on building and scaling end-to-end machine-learning and generative-AI platforms. Sitting at the intersection of engineering excellence and data-science enablement, you will design the core services, infrastructure and governance controls that allow hundreds of practitioners to prototype, deploy and monitor models, classical ML, deep learning and LLMs, securely and cost-effectively.

Acting as a "player-coach," you will establish platform strategy, define technical standards, and partner with DevOps, Security, Compliance and Product teams to deliver a frictionless, enterprise-grade AI developer experience.

Roles & Responsibilities

• Engineer end-to-end ML pipelines, data ingestion, feature engineering, training, hyper-parameter optimization, evaluation, registration and automated promotion, using Kubeflow, SageMaker Pipelines, Open AI SDK or equivalent MLOps stacks.
• Harden research code into production-grade micro-services, packaging models in Docker/Kubernetes and exposing secure REST, gRPC or event-driven APIs for consumption by downstream applications.
• Build and maintain full-stack AI applications by integrating model services with lightweight UI components, workflow engines or business-logic layers so insights reach users with sub-second latency.
• Optimize performance and cost at scale, selecting appropriate algorithms (gradient-boosted trees, transformers, time-series models, classical statistics), applying quantization/pruning, and tuning GPU/CPU auto-scaling policies to meet strict SLA targets.
• Instrument comprehensive observability, real-time metrics, distributed tracing, drift & bias detection and user-behavior analytics, enabling rapid diagnosis and continuous improvement of live models and applications.
• Embed security and responsible-AI controls (data encryption, access policies, lineage tracking, explainability and bias monitoring) in partnership with Security, Privacy and Compliance teams.
• Contribute reusable platform components, feature stores, model registries, experiment-tracking libraries, and evangelize best practices that raise engineering velocity across squads.
• Perform exploratory data analysis and feature ideation on complex, high-dimensional datasets to inform algorithm selection and ensure model robustness.
• Partner with data scientists to prototype and benchmark new algorithms, offering guidance on scalability trade-offs and production-readiness while co-owning model-performance KPIs.

Must-Have Skills

• 3-5 years in AI/ML and enterprise software.
• Comprehensive command of machine-learning algorithms, regression, tree-based ensembles, clustering, dimensionality reduction, time-series models, deep-learning architectures (CNNs, RNNs, transformers) and modern LLM/RAG techniques, with the judgment to choose, tune and operationalise the right method for a given business problem.
• Proven track record selecting and integrating AI SaaS/PaaS offerings and building custom ML services at scale.
• Expert knowledge of GenAI tooling: vector databases, RAG pipelines, prompt-engineering DSLs and agent frameworks (e.g., LangChain, Semantic Kernel).
• Proficiency in Python and Java; containerisation (Docker/K8s); cloud (AWS, Azure or GCP) and modern DevOps/MLOps (GitHub Actions, Bedrock/SageMaker Pipelines).
• Strong business-case skills, able to model TCO vs. NPV and present trade-offs to executives.
• Exceptional stakeholder management; can translate complex technical concepts into concise, outcome-oriented narratives.