Data Science and DevOps teams, ensuring effective AI/ML solution deployment Hands-On Support - Assist data scientists with DevOps issues, Docker containers, and MLOps tooling Model Deployment - Deploy HuggingFace Transformers and ML models as secure microservices AWS ML Platform - Build and evaluate models using SageMaker, Bedrock, Glue, Athena, and Redshift Knowledge Transfer - Create documentation and mentor … engineering, model evaluation Critical Technical Skills: Production ML deployment - Demonstrated experience maintaining AI/ML models in production AWS ML services - SageMaker, Bedrock, Glue, Kendra, Lambda, ECS Fargate, Redshift HuggingFace deployment - NLP, vision, and generative models in AWS environments API development - Flask, FastAPI microservices and REST API frameworks DevOps integration - CI/CD pipelines, Jenkins, Maven, Chef … Working across cloud-based infrastructures Tech Stack & Tools AWS Services: SageMaker, Bedrock, Glue, ECS Fargate, Athena, Kendra, RDS, Redshift, Lambda, CloudWatch Development: Python, R, Flask, FastAPI, SQL MLOps: Apigee, HuggingFace, Jenkins, Git, Docker Environments: Jupyter, RStudio, Linux What We're Looking For Experience Level: Sr. Associate or Manager with hands-on data analytics and software delivery experience More ❯
for data quality, lineage, privacy, and security, ensuring our AI systems are developed and used responsibly and ethically. Tooling the Future: Get hands-on with cutting-edge technologies like HuggingFace, PyTorch, TensorFlow, Apache Spark, Apache Airflow, and other modern data and ML frameworks. Collaborate and Lead: Partner closely with ML Engineers, Data Scientists, and Researchers to understand … Data Governance & Ethics: Experience implementing data governance frameworks, ensuring data quality, privacy, and compliance, with an awareness of ethical AI considerations. Bonus Points If You Have: Direct experience with HuggingFace ecosystem, PyTorch, or TensorFlow for data preparation in an ML context. Experience with real-time data streaming architectures. Familiarity with containerization (Docker, Kubernetes). Master's or More ❯
our product and community. Engineering Design, develop, and optimize AI/ML features in Qdrant's core engine and SDKs. Prototype and implement integrations with popular ML frameworks (e.g., HuggingFace, OpenAI, LangChain). Analyze performance, identify bottlenecks, and implement scalable solutions in real-world AI pipelines. Collaborate with product and engineering teams to define and deliver impactful … inform product development. Requirements Strong proficiency in Python. Solid understanding of machine learning concepts, embeddings, and vector search. Experience with at least one modern ML framework (e.g., PyTorch, TensorFlow, HuggingFace). Excellent communication skills; ability to explain technical topics to diverse audiences. Prior experience contributing to open-source projects or engaging with developer communities. Comfortable presenting and More ❯
on expertise building and deploying deep learning models (e.g., CNNs, Transformers, graph neural networks) in real-world applications. Proficiency in Python and core ML libraries (PyTorch, TensorFlow, scikit-learn, HuggingFace, etc.). Strong software engineering background: data structures, algorithms, distributed systems, and version control (Git). Experience designing scalable ML infrastructure on cloud platforms (AWS SageMaker, GCP More ❯
AI. Able to contextualise these techniques in industry settings, such as financial forecasting, operations optimisation, or customer segmentation. Comfortable with key ML frameworks (such as Scikit-learn, TensorFlow, PyTorch, HuggingFace) and data manipulation tools (Pandas, NumPy), as well as version control, containerisation, and ML deployment pipelines. Understands how to apply MLOps principles in production environments To be … essential. Staying updated on the latest trends and research is important. Motivation Techniques - Being able to motivate and inspire learners to stay committed to their programme, even in the face of challenges. Self-awareness - Ability to understand your own biases, values, and beliefs, which can impact your coaching approach. Time Management - Effective time management skills are essential for structuring More ❯
Qualifications and experience we consider to be essential for the role: Programming & Libraries: Deep proficiency in Python and extensive experience with relevant AI/ML/NLP libraries (e.g., HuggingFace Transformers, spaCy, NLTK). LLM Expertise: Proven experience developing applications leveraging state-of-the-art LLMs (e.g., GPT series, Llama series, Mistral, Claude) including prompt engineering, fine More ❯
Gemini, Llama, Falcon, Mistral. Model performance and optimization: Fine-tuning and optimizing LLMs for quality, latency, sustainability, and cost. Programming and NLP tools: Advanced Python, frameworks like PyTorch, TensorFlow, HuggingFace, LangChain. MLOps and deployment: Docker, Kubernetes, Azure ML Studio, MLFlow. Cloud and AI infrastructure: Experience with Azure Cloud for scalable deployment. Databases and data platforms: SQL, NoSQL More ❯
principles and version control (Git) Experience working in cloud environments (AWS, GCP, or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with HuggingFace Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g., MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI More ❯
London, South East, England, United Kingdom Hybrid / WFH Options
Opus Recruitment Solutions Ltd
principles and version control (Git) Experience working in cloud environments (AWS, GCP, or Azure) Ability to work independently and communicate effectively in a remote team Bonus Points Experience with HuggingFace Transformers , LangChain , or RAG pipelines Knowledge of MLOps tools (e.g. MLflow, Weights & Biases, Docker, Kubernetes) Exposure to data engineering or DevOps practices Contributions to open-source AI More ❯
resolve complex technical challenges in big data and ML systems. About You You're fluent in Python and experienced with leading ML/AI frameworks (PyTorch, TensorFlow, Scikit-learn, HuggingFace). You have a solid background in MLOps: experiment tracking, CI/CD for ML, model versioning, deployment, and monitoring. You've built scalable backend systems and More ❯
deployment tools (e.g., Docker, Kubernetes). Excellent problem-solving skills and the ability to work on complex AI challenges. Nice to Have: Experience with NLP frameworks, transformer architectures, and HuggingFace libraries. Knowledge of AI ethics, data privacy, and model interpretability. Previous experience working on AI-based translation, automation, or localization projects. Roadmap for Success First 90 Days More ❯
platforms such as AWS, GCP, or Azure Understanding of API integration and deploying solutions in cloud environments Familiarity or hands-on exposure to generative AI ecosystems (e.g., OpenAI, Bedrock, HuggingFace) LLMs & Emerging Tech Awareness Awareness of large language models (LLMs) and a strong enthusiasm for staying current with advancements in generative AI and applied machine learning Communication More ❯
data lake architectures, data integration, and data governance, and at least 2 years of experience with cloud-based AI/ML technologies (such as tools from AWS, Azure, Google, HuggingFace, OpenAI and Databricks) building ML or applied AI solutions. A passion for Generative AI, and an understanding of strengths and weaknesses of Generative LLM's Fundamental knowledge of ML, and More ❯
pipelines Working knowledge of embedding compliance and security in ML systems, including governance, access controls, and regulatory alignment (e.g., GDPR, HIPAA) Proficient with modern AI tooling and ecosystems, including HuggingFace, Cursor, vector DBs, and productivity tools that accelerate GenAI development Expertise in GenAI and LLMs, with hands-on experience in RAG solutions and agentic frameworks; capable of More ❯
define priorities and influence the product roadmap What we look for: Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch Experience building production-grade More ❯
define priorities and influence the product roadmap What we look for: Experience building Generative AI applications, including RAG, agents, text2sql, fine-tuning, and deploying LLMs, with tools such as HuggingFace, Langchain, and OpenAI Extensive hands-on industry data science experience, leveraging typical machine learning and data science tools including pandas, scikit-learn, and TensorFlow/PyTorch Experience building production-grade More ❯
running a large-scale cloud-native Machine Learning platform Extensive experience with programming languages such as Python, Java, Scala etc. Solid experience with ML frameworks such as Pytorch and Huggingface The ability to work in a team, collaborate with others to solve interesting problems that directly affect our customers Demonstrated critical thinking and problem-solving abilities, excellent communication and written More ❯
and implemention synchronous, asynchronous and batch data processing operations Expert level programming skills in Python, along with experience in using relevant tools and frameworks such as PyTorch, FastAPI and Huggingface; strong programming skills in Java are a plus Expert level know-how of ML Ops systems, data pipeline design and implementation, and working with ML platforms (preferably AWS SageMaker) Strong More ❯
how to fine-tune those models eg. XGBoost, Deep Neural Networks, Transformers, Markov chains, etc. Experience using specialized machine learning libraries e.g. Fastai, Keras, Tensorflow, pytorch, sci-kit learn, huggingface, etc. Must demonstrate the capacity of reading, understanding and implementing new techniques in the field of machine learning as they emerge. Experience of using Cloud technologies eg. AWS, GCP or More ❯
learning modelling techniquesand how to fine-tunethosemodelseg.XGBoost, Deep Neural Networks, Transformers,ResNets,VAEs, GANs,Markov chains, etc. Experience using specialized machine learning librarieseg.Fastai,Keras,Tensorflow,pytorch, sci-kit learn,huggingface,etc. Must demonstrate the capacity of reading, understanding and implementingnew techniques in the field of machine learning as they emerge. Experience of using Cloud technologieseg.AWS, GCP or Azure Specialisedvisualisationtechniqueseg.D3.js,ggplotetc. More ❯
and Demonstrate Build proof-of-concepts (PoCs) or demos to validate solution approaches and inspire client confidence. Showcase AI capabilities using relevant tools (e.g. Power BI + Azure ML, HuggingFace, LangChain, GPT APIs). 5. Track Industry Trends and Use Cases Stay ahead of market trends in Generative AI, Responsible AI, and emerging AI use cases across … ML concepts: supervised and unsupervised learning, GenAI, LLMs, embeddings, MLOps, vector search. Experience designing solutions using tools such as Azure ML, AWS SageMaker, Google Vertex AI, Databricks, LangChain, and Hugging Face. Ability to develop architecture artefacts (e.g., HLDs), estimate effort and cost, and contribute to proposal writing. Strong storytelling and presentation skills; able to build confidence with senior business More ❯
big data engineering to productionizing classical ML and LLMs, you'll be at the core of AI infrastructure. Key Requirements: Expert in Python, ML/AI frameworks (PyTorch, TensorFlow, HuggingFace) Proven MLOps, big data, and backend/API development experience Deep understanding of NLP and LLMs Proficient with cloud platforms (AWS/GCP/Azure), Airflow, DBT More ❯
big data engineering to productionizing classical ML and LLMs, you'll be at the core of AI infrastructure. Key Requirements: Expert in Python, ML/AI frameworks (PyTorch, TensorFlow, HuggingFace) Proven MLOps, big data, and backend/API development experience Deep understanding of NLP and LLMs Proficient with cloud platforms (AWS/GCP/Azure), Airflow, DBT More ❯
s degree in AI and/or Computer Science; Hands-on experience integrating LLM APIs (e.g. OpenAI, HuggingFace Inference); Practical experience fine-tuning LLMs via OpenAI, HuggingFace or similar APIs; Strong proficiency in Python; Deep expertise in prompt engineering and tooling like LangChain or LlamaIndex; Proficiency with vector databases (Pinecone, FAISS, Weaviate) and document embedding pipelines; Proven More ❯
or related technical field 2+ years of experience in AI/ML development with a focus on practical applications Strong proficiency in Python and relevant AI libraries (TensorFlow, PyTorch, HuggingFace) Hands-on experience with workflow automation platforms like N8N, AirTable, and proven track-record. Experience with AI agent development and testing methodologies using Google ADK, LangGraph, Llamaindex More ❯
