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Mlflow Developer Jobs

4 jobs found

10d ago

Senior Machine Learning Engineer

Siemens Raleigh, NC

Raleigh, NC

10d ago

Full time Senior AI Ai Ml

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Job Description

Siemens invites a Senior Machine Learning Engineer to drive LLM driven applications on AWS, overseeing production‑grade ML and LLM services and owning the complete model lifecycle with modern MLOps practices. This onsite role is based in Raleigh, North Carolina.

Responsibilities

Build LLM applications by designing and implementing retrieval augmented generation pipelines, prompt orchestration, tools and agents, safety guardrails, and evaluation harnesses; instrument for latency, cost, and quality.
Own the ML lifecycle from data curation and feature engineering through training and fine‑tuning (LoRA/QLoRA), A/B testing, deployment, monitoring, and ongoing improvement of models and prompts.
Productionize on AWS by shipping scalable services on EKS, ECS, and Lambda; leverage SageMaker, Bedrock, EMR, MSK, and Step Functions; implement observability via CloudWatch and OpenTelemetry and apply cost controls.
Lead MLOps and governance efforts by establishing CI/CD for models (MLflow, Kedro, SageMaker Pipelines), model/version registries, data and prompt lineage, evaluation gates, and responsible‑AI controls.
Collaborate with Brightly stakeholders to translate asset‑management use cases into ML/LLM solutions, working with product managers and UX to deliver customer‑facing features that improve reliability, safety, and sustainability.
Perform exploratory data analysis on structured, semi‑structured, and unstructured data to uncover patterns, correlations, feature importance, and data quality concerns.
Conduct in‑depth research on asset‑related, operational, and domain datasets to identify root causes, trends, and predictive signals.
Operate in a pragmatic, product‑oriented manner, focusing on measurable outcomes and rapid iteration with stakeholders.
Demonstrate engineering excellence by delivering production‑quality Python, designing reliable APIs and services, and upholding testing and observability standards.
Provide collaborative leadership by mentoring peers and influencing architectural decisions across teams.

Requirements

8–10 years of total software or ML engineering experience, including 2+ years building and operating ML systems in production.
1+ year hands‑on experience developing LLM applications (RAG, fine‑tuning, prompt engineering, evaluators/guardrails, agentic workflows) using Langchain and Langgraph.
AWS proficiency (3+ years) with core services (EKS/ECS, Lambda, S3, DynamoDB or RDS, Step Functions, IAM) and ML stack (SageMaker, Bedrock or HF on AWS).
Modeling and frameworks: Python, PyTorch, Hugging Face ecosystem; experience with vector stores (OpenSearch, PGVector, Pinecone), embeddings, retrieval, and NLP/LLM evaluation metrics.
MLOps: CI/CD for ML, model registries, experiment tracking, telemetry/monitoring, automated retraining; Docker/Kubernetes; GitHub Actions or GitLab CI.
Data engineering fluency: ETL/ELT, streaming and batch processing (Spark/Flink), and data quality and governance controls for ML.
Education: Bachelor’s degree required, Master’s degree preferred.

Technologies

Python
PyTorch
Hugging Face ecosystem
Langchain, Langgraph
AWS (SageMaker, Bedrock, EKS, ECS, Lambda, S3, DynamoDB, RDS, Step Functions, EMR, MSK)
OpenSearch, PGVector, Pinecone
Spark, Flink
Cloud monitoring: CloudWatch, OpenTelemetry
Docker, Kubernetes
GitHub Actions, GitLab CI
MLflow, Kedro, SageMaker Pipelines

Nice to Have

Experience with distributed training (FSDP, DeepSpeed), RLHF, or Inferentia/Trainium optimization.
Exposure to sustainability, asset management, or intelligent operations domains.
Familiarity with security and regulatory compliance considerations for ML systems in enterprise environments.

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30d ago

Lead Data Engineer

INSPYR Solutions Doraville, GA 🇺🇸 Remote

Doraville, GA

30d ago Closed

$60 - $70/hr Contract Artificial Intelligence Big Data Bigdata

This position is no longer accepting applications (closed Aug 12, 2026).

Job Description

Lead Data Engineer at INSPYR Solutions will architect and optimize distributed data pipelines, establish Lakehouse architecture, enable ML workflows, ensure data quality, and improve production reliability.

Responsibilities

Architect, build, and optimize distributed data pipelines using Apache Spark in a high-volume, mission-critical environment.
Design and maintain enterprise Lakehouse infrastructure with Delta Lake, ensuring ACID compliance, lineage, auditability, and governance.
Develop automated ingestion frameworks (batch, streaming, and event-driven) across multiple cloud services and integration points.
Prepare feature-ready datasets and establish reproducible machine learning deployment patterns to enable ML workflows.
Lead platform-wide data quality, access control, and cataloging frameworks.
Implement cost optimization, cluster tuning, and performance engineering strategies.
Collaborate with Finance, BI, Operations, and ML teams to translate complex business needs into scalable data solutions.
Own production reliability, troubleshooting, and root cause analysis for data and ML pipelines.

Requirements

7+ years of experience in advanced data engineering with distributed compute technologies.
Expert Spark engineering experience including performance tuning, cluster configuration, partition strategies, and large dataset optimization.
Hands-on experience with Lakehouse architectures featuring ACID transactions, schema evolution, and governance frameworks.
Strong Python and SQL skills for large-scale data transformations.
Experience supporting machine learning pipelines or model operationalization.
Proven track record architecting cloud-native data platforms on Azure, AWS, or GCP.
Experience integrating diverse, complex data sources at enterprise scale.
Demonstrated ability to own mission-critical production systems.
Experience with distributed streaming frameworks such as Kafka or Event Hubs.
Experience building or supporting ML platforms, feature stores, or experiment tracking systems.
Background in data security, compliance controls, or audit-ready governance.
Experience automating data operations with CI/CD and infrastructure as code.

Technologies

Databricks, Python, SQL
Apache Spark, Delta Lake
MLflow, Notebooks
Hugging Face Transformers, LangChain, LlamaIndex
LLMs: Anthropic Claude, Meta LLaMA, Google Gemini
Kafka, Event Hubs, ADLS, S3, GCS
Git, GitHub, GitLab, Azure Repos, Databricks Repos
GitHub Actions, Azure DevOps
Unity Catalog, RBAC, REST APIs

Benefits

Comprehensive medical benefits
Competitive pay
401(k) retirement plan

Core Tools

Databricks (Spark, Delta Lake, MLflow, Notebooks)
Python & SQL
Apache Spark via Databricks
Delta Lake for lakehouse architecture

Cloud Platforms

Azure, AWS, or GCP
Cloud storage options: ADLS, S3, GCS

Data Integration

Kafka or Event Hubs for streaming data
Auto Loader for Databricks file ingestion
REST APIs
AI/ML and MLflow for model tracking and deployment
Hugging Face Transformers, LangChain, LlamaIndex for LLM integration
LLMs: Anthropic Claude, Meta LLaMA, Google Gemini

DevOps

Git-based version control (GitHub, GitLab, Azure Repos)
Databricks Repos for notebooks and code
CI/CD: GitHub Actions, Azure DevOps

Security & Governance

Unity Catalog for data governance
RBAC to manage access controls

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1mo ago

Senior Machine Learning Engineer

Leidos Vista, CA

Vista, CA

1mo ago Closed

$108k - $195k/yr Full time Ai Ml Artificial Intelligence Computer Vision

This position is no longer accepting applications (closed Jul 26, 2026).

Job Description

This onsite role in Vista, California offers a competitive annual salary ranging from USD 107,900 to 195,050, plus a comprehensive benefits package. You will join Leidos as a Senior Machine Learning Engineer focused on MLOps driven object detection for border security, leading the end-to-end lifecycle of models from development through deployment and integration with operational systems. The position places a premium on cross-functional collaboration, reliable delivery, and impact across mission-critical systems.

What you will do

Develop, train, and assess machine learning models using modern MLOps practices and frameworks.
Design and maintain reproducible training pipelines that enable scalable experimentation and clear traceability.
Collaborate with cross-functional teams to embed models into live workflows and operational systems.
Improve model performance and dependability through continuous monitoring, testing, and iteration.

What you bring

MS or PhD in data science, engineering, applied science, or a related field, plus at least 10 years of industry experience.
Capability to support the full ML lifecycle, from data preparation and model training to deployment and monitoring.
Experience tracking experiments, evaluating model performance, and managing model versions with a platform such as MLflow to ensure transparency and auditability.
Experience with data versioning tools like DVC, MLFlow Dataset, or LakeFs.
Experience deploying and operating ML models in production environments using Docker and Kubernetes.
Familiarity with modern data stacks including cloud platforms, data warehouses, and MLOps concepts.
Strong ability to evaluate technical approaches and guide decision-making.
A proven track record of owning delivery and collaborating across functions.
Ability to multitask across concurrent projects and priorities.
Excellent written and verbal communication skills.
Some travel required up to 25% to support projects.
Ability to obtain and maintain Public Trust access.

Technologies

MLflow
DVC
MLFlow Dataset
LakeFs
Docker
Kubernetes
Kubeflow
Airflow
ResNet
Yolo
U-Net

Benefits

Competitive compensation
Health and Wellness programs
Income Protection
Paid Leave
Retirement

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1mo ago

Machine Learning Engineer, Motion Planning & Prediction

Avride Austin, TX

Austin, TX

1mo ago Closed

Full time Ai Ml Artificial Intelligence Big Data

This position is no longer accepting applications (closed Jul 16, 2026).

Job Description

Avride is seeking a Machine Learning Engineer to join its autonomous vehicle team in Austin, TX on site, focusing on motion planning and prediction. The role entails building end-to-end ML models, developing data pipelines, and enabling real-time inference on embedded hardware, while applying cutting-edge ML research.

Responsibilities

Develop and operationalize advanced machine learning models for behavioral prediction and motion planning
Build scalable data pipelines to process, clean, and label large-scale vehicle sensor and simulation datasets
Leverage architectures such as transformers to capture temporal interactions among traffic agents
Define and own model performance metrics and create evaluation frameworks aligned with on-road safety and performance
Collaborate with software engineers to integrate models and optimize real-time inference on embedded vehicle hardware
Maintain awareness of the latest ML research, including imitation learning and reinforcement learning, and apply novel techniques to systems

Requirements

Proficient in Python with hands-on experience in modern deep learning frameworks (PyTorch, TensorFlow, or JAX)
Solid understanding of ML fundamentals, including neural network architectures, training methodologies, and evaluation techniques
Experience across the full ML lifecycle, from data exploration and prototyping to deployment and monitoring
C++ proficiency for writing high-performance model inference code

Technologies

Python
PyTorch
TensorFlow
JAX
C++
MLflow
Kubeflow
Weights & Biases
Spark
Ray

Nice to have

Strong track record in ML competitions (for example Kaggle) or contributions to major open-source ML projects
Experience applying ML to robotics problems, such as behavioral prediction, motion planning, or computer vision
Familiarity with MLOps tools and platforms (MLflow, Kubeflow, Weights & Biases)
Experience with large-scale distributed data processing and training frameworks (Spark, Ray)
Publications in top-tier ML or robotics conferences (NeurIPS, ICML, CVPR, ICLR, CoRL, RSS)

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