software engineering or data/AI engineering, with at least 5+ years focused on AI and ML system development. Strong expertise in machine learning algorithms, deep learning frameworks (TensorFlow, PyTorch, Keras), and classical ML tools (Scikit-learn Hands-on experience building production-grade AI and data pipelines using cloud platforms (AWS, Azure, or Google Cloud Proficiency in Python, SQL, and More ❯
optimization, scenario analysis, and statistical methodologies. Strong grasp of supervised/unsupervised methods, evaluation metrics, feature engineering, and model tuning. Proficiency in Python (pandas, NumPy, Scikit-learn); experience with PyTorch or TensorFlow for deep learning. Experience with API development and connecting AI systems to external platforms. Working knowledge in deep learning techniques, including CNNs, RNNs, and transformers. Hands-on experience More ❯
asyncio for concurrent processing, and building robust automation scripts with proper logging, testing (pytest), and documentation. AI & Machine Learning Frameworks - Deep expertise in AI/ML frameworks including TensorFlow, PyTorch, Scikit-learn, and Hugging Face Transformers. Experience building, training, and deploying machine learning models for classification, regression, clustering, and NLP tasks. Understanding of model evaluation metrics, hyperparameter tuning, feature engineering More ❯
a strong portfolio of high-impact projects in production Expert-level programming skills in Python and SQL, and fluency with leading ML/AI frameworks (e.g., scikit-learn, TensorFlow, PyTorch) Direct experience with GenAI/LLM technologies, including tools like Hugging Face, LangChain, OpenAI APIs, vector databases, and fine-tuning methods Deep knowledge of machine learning algorithms (supervised, unsupervised, deep More ❯
secure coding principles Familiarity with geospatial libraries such as GeoPandas, Shapely, and GDAL Knowledge of PostgreSQL/PostGIS for spatial data management Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Experience working with large datasets, including data preprocessing Excellent More ❯
adoption of AI/ML practices. Qualifications Advanced proficiency in Python (specifically for machine learning) and extensive experience with core AI/ML open-source libraries, including scikit-learn, PyTorch, pandas, polars, NumPy, and seaborn. Proven experience designing and deploying end-to-end AI/ML systems, with a strong emphasis on MLOps principles and tools (Docker, Kubernetes, Git). More ❯
SageMaker, Azure ML) Proven experience developing or deploying AI models across domains such as natural language processing, computer vision, or predictive analytics Familiarity with machine learning frameworks (e.g., TensorFlow, PyTorch, Scikit-learn) and data science tools (e.g., Jupyter, Pandas, NumPy) Ability to design, train, and evaluate supervised and unsupervised learning algorithms Strong teamwork and interpersonal skills, with a collaborative and More ❯
Hands-on experience with RAG architectures, including document chunking, embedding generation, and retrieval systems. Proficiency in Python and familiarity with libraries such as Hugging Face, Transformers, OpenAI API, and PyTorch or TensorFlow. Experience with cloud platforms (AWS, Azure, GCP) and containerization (Docker, Kubernetes Strong understanding of LLM capabilities, limitations, and prompt engineering techniques. Preferred Qualifications: Experience with fine-tuning LLMs More ❯
Functions, etc. Proven expertise in MLOps implementation for deploying, monitoring, and managing ML models in production environments. Proficiency in Python and experience with ML frameworks and libraries (e.g., TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy Strong understanding of cloud technologies and AI/ML platforms, particularly AWS SageMaker. Solid grasp of software engineering principles including design patterns, testing, CI/CD More ❯
data processing libraries (e.g., Pandas, Polars, NumPy). Hands-on experience in building and maintaining automated data pipelines for large-scale data processing. Familiarity with machine learning frameworks (e.g., PyTorch, JAX, scikit-learn) as applied to data quality and augmentation tasks. Expertise in working with healthcare data, including familiarity with the OMOP Common Data Model (OMOP CDM). Strong experience More ❯
Austin, Texas, United States Hybrid/Remote Options
OSI Engineering
/ML Data, Tools Development Proficiency in Python, with a background in back end services and data processing Exposure to AI/ML algorithms Familiarity with ML frameworks (TensorFlow, PyTorch, scikit-learn) Understanding of LLMs, vector databases, and retrieval systems Experience with Model Context Protocol (MCP) integration and server development Big Data & Cloud Infrastructure Knowledge of building and deploying cloud More ❯
Gemini, or Perplexity. Strong understanding of generative AI principles, prompt chaining, context window management, and token efficiency. Proficiency in Python and experience with AI/ML frameworks like TensorFlow, PyTorch, or Hugging Face. Experience integrating AI into enterprise platforms such as DealCloud, Salesforce, or similar CRMs. Understanding of workflow automation tools (e.g., Zapier, n8n, Make) and dashboarding platforms (e.g., Power More ❯
fairfax, virginia, united states Hybrid/Remote Options
JANSON
with at least 2 years supporting federal or defense programs. * Must possess proficiency with *Maven, Vantage, TDP and Advana.* * Proficiency in Python and ML libraries (e.g., scikit-learn, TensorFlow, PyTorch) Experience with data wrangling, feature engineering, and model evaluation in secure or air-gapped environments. * Familiarity with DoD data systems, cybersecurity protocols, and cloud platforms (e.g., AWS GovCloud, C2S, Azure More ❯
at least one AI-focused programming language (Python required; Java preferred Strong experience with cloud platforms such as AWS, Azure, or GCP. Solid understanding of machine learning frameworks (TensorFlow, PyTorch, scikit-learn Experience working with CI/CD platforms (Jenkins, GitHub, GitLab CI, Artifactory Hands-on experience with automated vulnerability detection and remediation using DAST/SAST/IAST scanning More ❯
e.g., GIS, ArcGIS), remote sensing techniques, and the application of data science in the IC. Expert proficiency in Python (or similar languages) and experience with data science libraries (TensorFlow, PyTorch, Pandas, NumPy). Strong experience with big data processing tools (e.g., Spark, Hadoop, AWS or Azure cloud platforms). Expertise in working with geospatial data formats (e.g., GeoTIFF, Shapefiles, WMS More ❯
years of focused experience in AI and data-driven architecture design. • Extensive experience in enterprise architecture frameworks (e.g., TOGAF, Zachman). • Expertise in AI/ML technologies (e.g., TensorFlow, PyTorch, Scikit-learn, Keras) and understanding of deep learning and natural language processing (NLP). • Strong understanding of big data platforms such as Hadoop, Apache Spark, and data lakes. • Hands-on More ❯
associated concepts, such as transformer architecture and retrieval augmented generation. Strong programming ability with demonstrated experience in Python and one or more associated machine learning frameworks, such as TensorFlow, PyTorch, or SKLearn. Knowledge of and experience working with open-source AI models. Demonstrated ability to perform the essential duties of the position with or without accommodation. Authorization to work in More ❯
Azure Data Factory, Synapse Analytics, and related Microsoft AI/ML tools. Knowledge of cloud-based MLOps practices. Strong SQL and data modelling skills. Experience with deep learning frameworks (PyTorch, TensorFlow) is nice to have. Knowledge of containerization (Docker, Kubernetes) is nice to have. More ❯
experience with Databricks, PySpark, Delta Lake, MLflow . Experience with LLMs (Hugging Face, LangChain, Azure OpenAI) . Strong MLOps, CI/CD, and model monitoring experience. Proficiency in Python, PyTorch/TensorFlow, FastAPI/Flask . Cloud architecture experience: Azure preferred, AWS/GCP acceptable . Skilled in Docker, Kubernetes, Helm, Terraform, IaC for deploying ML and web apps. More ❯
vector databases, embeddings, retrieval optimisation) Academic Excellence: BSc or MSc in Computer Science, AI, Data Science, or related technical field from a top university Familiarity with frameworks such as PyTorch, Hugging Face, LangChain, or LlamaIndex Passionate about sports, health, or fitness – you’ll feel right at home here Bonus points if you: Have worked with unstructured data (text, documents, time More ❯
and software 5+ years of experience as an ML Engineer or Software Engineer with ML focus 5+ years of proficient experience with Python, strong experience in ML frameworks (TensorFlow, PyTorch, Scikit-learn) Experience with distributed training and optimization on GPUs (CUDA, RAPIDS) Familiarity with data pipelines (Spark, Databricks, Kafka) Hands-on experience with CI/CD for ML workflows and More ❯
familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed More ❯
London, South East, England, United Kingdom Hybrid/Remote Options
Method Resourcing
familiarity with core ML concepts (classification, time-series, statistical modeling) and their real-world tradeoffs. Fluency in Python and commonly used ML libraries (e.g. pandas, scikit-learn; experience with PyTorch or TensorFlow is a plus). Experience with model lifecycle management (MLOps), including monitoring, retraining, and model versioning. Ability to work across data infrastructure, from SQL to large-scale distributed More ❯
data & AI stacks Proficiency in Python and SQL; additional experience with other languages is a plus Experience working with tools and frameworks such as Spark, Kafka, Airflow, MLflow, TensorFlow, PyTorch, Scikit-learn, etc. Excellent communication and interpersonal skills; ability to work with both technical and business stakeholders. Strong command of German and English language (both verbal and written) Comfortable with More ❯
