Senior Machine Learning Engineer

United States of America

Full Time

2 days ago

H1B Sponsor Likely

Key skills

Large Language Model (LLM) application developmentRetrieval-Augmented Generation (RAG) pipelinesPrompt engineeringFine-tuning (LoRA/QLoRA)A/B testingAWS core services EKSAWS core services ECSAWS core services LambdaAWS core services S3AWS core services DynamoDBAWS core services RDSAWS core services Step FunctionsAWS core services IAMAWS ML services SageMakerAWS ML services BedrockAWS ML services EMRAWS ML services MSKPythonPyTorchHugging Face ecosystemVector stores OpenSearchVector stores PGVectorVector stores PineconeNLP/LLM evaluation metricsMLOps CI/CD for MLMLOps model registriesMLOps experiment trackingMLOps telemetryMLOps automated retrainingDockerKubernetesGitHub ActionsGitLab CIData engineering ETL/ELTData engineering SparkData engineering FlinkEngineering excellencePragmatic and product-orientedCollaborative leadershipAIMachine LearningMLNLPLLMRAGLangChainAgenticHugging FaceMLOpsMLflowLangGraphPineconeETLELTData EngineeringSparkAWSECSEKSLambdaS3DynamoDBRDSIAMCloudWatchSageMakerBedrockOpenTelemetryGitHubGitLabCI/CDA/B Testing

About this role

Siemens is seeking a Senior Machine Learning Engineer to lead LLM‑powered application development on AWS. The role involves designing robust ML/LLM services and collaborating with product, data, and engineering teams to enhance the Brightly platform.

Responsibilities:

Build LLM applications: Design and implement RAG pipelines, prompt orchestration, tools/agents, safety/guardrails, and evaluation harnesses; instrument for latency, cost, and quality. (Guided by current LLM engineer role practices.)
Own the ML lifecycle: Data curation, feature engineering, training/fine‑tuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and continuous improvement of models and prompts
Productionize on AWS: Ship scalable services on EKS/ECS/Lambda; leverage SageMaker, Bedrock, EMR, MSK, Step Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties aligned to modern AWS ML roles.)
MLOps & governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsible‑AI controls. (Aligned with contemporary MLOps templates.)
Partner across Brightly: Translate asset‑management use cases into ML/LLM solutions; collaborate with product managers and UX to ship customer‑visible features that measurably improve reliability, safety, and sustainability
Perform Exploratory Data Analysis (EDA) on structured, semi‑structured, and unstructured datasets to identify patterns, correlations, feature importance, and data quality issues. (Consistent with ML engineer responsibilities to analyze data before model development.)
Conduct deep research on asset-related, operational, and domain-specific datasets to understand root causes, trends, and predictive signals

Requirements:

8-10 years total software/ML engineering experience, with 2+ years building and operating ML systems in production
1+ years hands‑on LLM application development (e.g., RAG, fine‑tuning, prompt engineering, evaluators/guardrails, agentic workflows) using packages such as Langchain and Langgraph
AWS proficiency (3+ years): Strong with core services (EKS/ECS, Lambda, S3, DynamoDB/RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS)
Modeling & frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs
MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI
Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML
Bachelor's in CS/EE/Math or related field (Master's preferred) or equivalent practical experience
Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization
Exposure to sustainability/asset/intelligent operations domains
Familiarity with security & compliance for ML systems in enterprise environments