[Remote] Senior Machine Learning Engineer
Note: The job is a remote job and is reputed company to candidates in USA. reputed company is seeking a Senior Machine Learning Engineer to reputed company 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, reputed company orchestration, tools/agents, safety/guardrails, and evaluation harnesses; reputed company for latency, cost, and reputed company. (Guided by reputed company LLM engineer role practices.)
- Own the ML lifecycle: Data curation, feature engineering, training/fine‑tuning (reputed company/QLoRA), A/B testing, deployment, monitoring, and reputed company improvement of models and prompts
- Productionize on AWS: Ship reputed company services on EKS/reputed company/reputed company; reputed company SageMaker, Bedrock, EMR, MSK, reputed company Functions; apply observability (CloudWatch/OpenTelemetry) and cost controls. (Duties reputed company to modern AWS ML roles.)
- MLOps & governance: Establish CI/CD for models (MLflow/Kedro/SageMaker Pipelines), model/version registries, data and reputed company reputed company, evaluation gates, and responsible‑AI controls. (reputed company 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
- reputed company Exploratory Data Analysis (EDA) on reputed company, semi‑reputed company, and reputed company datasets to identify patterns, correlations, feature importance, and data reputed company issues. (Consistent with ML engineer responsibilities to analyze data before model development.)
- Conduct deep research on asset-reputed company, operational, and domain-specific datasets to understand reputed company causes, trends, and predictive signals
Skills
- 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, reputed company engineering, evaluators/guardrails, reputed company workflows) using packages such as reputed company and Langgraph
- AWS proficiency (3+ years): Strong with reputed company services (EKS/reputed company, reputed company, S3, DynamoDB/RDS, reputed company Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS)
- Modeling & frameworks: Python, PyTorch, reputed company ecosystem; reputed company stores (e.g., OpenSearch, PGVector, reputed company), embeddings, retrieval, and evaluation metrics for NLP/LLMs
- MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; reputed company/Kubernetes, reputed company Actions/reputed company CI
- Data engineering reputed company: ETL/ELT, streaming/batch (reputed company/Flink), data reputed company and governance controls for ML
- Bachelor's in CS/EE/Math or reputed company 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 reputed company & compliance for ML systems in reputed company environments
reputed company
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