Senior Machine Learning Engineer

Siemens AG

New York, NY, United States

14 days ago

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Role details

Contract type

Permanent contract

Employment type

Part-time (≤ 32 hours)

Experience level

Expert

Experience required

1 year minimum

Working hours

Regular working hours

Job source

arc.dev

Tech stack

LangGraph Framework A/B Testing Application Programming Interfaces (APIs) Amazon Web Services Amazon S3 Data Analysis Continuous Integration Information Engineering Extract Transform Load (ETL) Distributed Computing Environment Amazon DynamoDB Github

+29 more

Identity and Access Management Python (Programming Language) Machine Learning Software Engineering Unstructured Data Pinecone QLoRA Graphics Processing Unit (GPU) Feature Engineering Pytorch DeepSpeed Retrieval-Augmented Generation Large Language Models Prompt Engineering Apache Spark Caching Discretization AWS Trainium Pgvector Gitlab-ci Kubernetes Apache Flink HuggingFace Machine Learning Operations Feature Importance Functional Programming Cloudwatch OpenSearch Docker

Job description

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.)
Scale training & inference: Use distributed training (FSDP/DeepSpeed), quantization, caching, vector databases, and GPU/Inferentia for performance and efficiency.
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.
Mentor & lead: Provide technical leadership, review designs/PRs, and raise the bar on ML engineering excellence across the team. (Common senior ML expectations.)
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., * Pragmatic and product-oriented: You bias to measurable outcomes and iterate quickly with stakeholders. (Modern senior ML role framing.)
Engineering excellence: You write production-quality Python, design reliable APIs/services, and uphold testing/observability standards. (Common duties in senior templates.)
Collaborative leadership: You mentor peers and influence architecture across teams. (Industry-standard senior expectations.)

Requirements

5-7 years total software/ML engineering experience, with 3+ 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). (Representative AWS ML role skills.)
Modeling & frameworks: Python, PyTorch, Hugging Face ecosystem; vector stores (e.g., OpenSearch, PGVector, Pinecone), embeddings, retrieval, and evaluation metrics for NLP/LLMs. (In line with senior LLM roles.)
MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes, GitHub Actions/GitLab CI. (Current MLOps expectations.)
Data engineering fluency: ETL/ELT, streaming/batch (Spark/Flink), data quality and governance controls for ML.
Nice to have
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. (Frequently included in senior ML roles.), * Bachelor’s in CS/EE/Math or related field (Master’s preferred) or equivalent practical experience. (Typical for senior ML roles.)

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