Note: The job is a remote job and is open to candidates in USA. Siemens is seeking a Senior Machine Learning Engineer to build technology that keeps communities running safer, greener, and more resilient. The role leads LLM-powered application development from prototype to production on AWS, owning the ML lifecycle, MLOps, scalable training and inference, and cross-functional delivery of asset-management solutions.
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.)
- 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
- 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.)
Skills
- • 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
- • Bachelor's in CS/EE/Math or related field or equivalent practical experience. (Typical for senior ML roles.)
- • 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.)
- • Master's
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