3 Tf Serving Jobs

As a Staff Machine Learning Engineer at Onclusive, you will be responsible for leading machine learning projects from experimentation to deployment. You will play a crucial role in integrating machine learning solutions into our production systems, driving the success of the company. Additionally, this role provides an opportunity for you to not only enhance your technical skills but also grow as a leader. Your primary responsibilities will include owning machine learning projects end-to-end, collaborating with ML leadership and stakeholders to ensure project delivery aligns with requirements and constraints. You will design and develop scalable machine learning services, research and implement advanced algorithms for data analysis, and work closely with data platform, product, and MLOps engineers to deploy ML/AI solutions effectively. Key qualifications for this role include a degree in Computer Science or a related field, along with 5+ years of experience in machine learning research. You should also have at least 3 years of experience in Natural Language Processing (NLP) with a focus on neural network approaches such as BERT, BART, and RoBERTa. Proficiency in cloud computing, particularly AWS, as well as familiarity with modern ML pipeline tools like Git, Docker, and Kubernetes are essential. Moreover, you should have a strong understanding of frameworks like Pytorch, Torchserve, TensorFlow, and platforms like Kubeflow and MLflow. Joining our team means being part of a fast-growing global company that values your professional growth. In return for your contributions, we offer a competitive salary and benefits package, a hybrid working environment, and a supportive team culture dedicated to your development. Our focus on wellbeing and work-life balance includes initiatives like flexible working arrangements and mental health support. If you are passionate about machine learning and eager to take on challenging projects in a dynamic environment, this role at Onclusive offers an exciting opportunity to make a significant impact and further your career.,

As an AI/ML Engineer, your primary responsibility will be to collaborate effectively with cross-functional teams, including data scientists and product managers. Together, you will work on acquiring, processing, and managing data for the integration and optimization of AI/ML models. Your role will involve designing and implementing robust, scalable data pipelines to support cutting-edge AI/ML models. Additionally, you will be responsible for debugging, optimizing, and enhancing machine learning models to ensure quality assurance and performance improvements. Operating container orchestration platforms like Kubernetes with advanced configurations and service mesh implementations for scalable ML workload deployments will be a key part of your job. You will also design and build scalable LLM inference architectures, employing GPU memory optimization techniques and model quantization for efficient deployment. Engaging in advanced prompt engineering and fine-tuning of large language models (LLMs) will be crucial, with a focus on semantic retrieval and chatbot development. Documentation will be an essential aspect of your work, involving the recording of model architectures, hyperparameter optimization experiments, and validation results using version control and experiment tracking tools like MLflow or DVC. Researching and implementing cutting-edge LLM optimization techniques such as quantization and knowledge distillation will be part of your ongoing tasks to ensure efficient model performance and reduced computational costs. Collaborating closely with stakeholders to develop innovative natural language processing solutions, with a specialization in text classification, sentiment analysis, and topic modeling, will be another significant aspect of your role. Staying up-to-date with industry trends and advancements in AI technologies and integrating new methodologies and frameworks to continually enhance the AI engineering function will also be expected of you. In terms of qualifications, a Bachelor's degree in any Engineering stream is required, along with a minimum of 4+ years of relevant experience in AI. Proficiency in Python with expertise in data science libraries (NumPy, Pandas, scikit-learn) and deep learning frameworks (PyTorch, TensorFlow) is essential. Experience with LLM frameworks, big data processing using Spark, version control, and experiment tracking, as well as proficiency in software engineering and development, DevOps, infrastructure, cloud services, and LLM project experience are also necessary. Your expertise should include a strong mathematical foundation in statistics, probability, linear algebra, and optimization, along with a deep understanding of ML and LLM development lifecycle. Additionally, you should have expertise in feature engineering, embedding optimization, dimensionality reduction, A/B testing, experimental design, statistical hypothesis testing, RAG systems, vector databases, semantic search implementation, and LLM optimization techniques. Strong analytical thinking, excellent communication skills, experience translating business requirements into data science solutions, project management skills, collaboration abilities, dedication to staying current with the latest ML research, and the ability to mentor and share knowledge with team members are essential competencies for this role.,

Strong in Python with libraries such as polars, pandas, numpy, scikit-learn, matplotlib, tensorflow, torch, transformers • Must have: Deep understanding of modern recommendation systems including two-tower , multi-tower , and cross-encoder architectures • Must have: Hands-on experience with deep learning for recommender systems using TensorFlow , Keras , or PyTorch • Must have: Experience generating and using text and image embeddings (e.g., CLIP , ViT , BERT , Sentence Transformers ) for content-based recommendations • Must have: Experience with semantic similarity search and vector retrieval for matching user-item representations • Must have: Proficiency in building embedding-based retrieval models , ANN search , and re-ranking strategies • Must have: Strong understanding of user modeling , item representations , temporal/contextual personalization • Must have: Experience with Vertex AI for training, tuning, deployment, and pipeline orchestration • Must have: Experience designing and deploying machine learning pipelines on Kubernetes (e.g., using Kubeflow Pipelines , Kubeflow on GKE , or custom Kubernetes orchestration ) • Should have experience with Vertex AI Matching Engine or deploying Qdrant , F AISS , ScaNN , on GCP for large-scale retrieval • Should have experience working with Dataproc (Spark/PySpark) for feature extraction, large-scale data prep, and batch scoring • Should have a strong grasp of cold-start problem solving using metadata and multi-modal embeddings • Good to have: Familiarity with Multi-Modal Retrieval Models combining text, image, and tabular features • Good to have: Experience building ranking models (e.g., XGBoost , LightGBM , DLRM ) for candidate re-ranking • Must have: Knowledge of recommender metrics (Recall@K, nDCG, HitRate, MAP) and offline evaluation frameworks • Must have: Experience running A/B tests and interpreting results for model impact • Should be familiar with real-time inference using Vertex AI , Cloud Run , or TF Serving • Should understand feature store concepts , embedding versioning , and serving pipelines • Good to have: Experience with streaming ingestion (Pub/Sub, Dataflow) for updating models or embeddings in near real-time • Good to have: Exposure to LLM-powered ranking or personalization , or hybrid recommender setups • Must follow MLOps practices version control, CI/CD, monitoring, and infrastructure automation GCP Tools Experience: ML & AI : Vertex AI, Vertex Pipelines, Vertex AI Matching Engine, Kubeflow on GKE, AI Platform Embedding & Retrieval : Matching Engine, FAISS, ScaNN, Qdrant, GKE-hosted vector DBs (Milvus) Storage : BigQuery, Cloud Storage, Firestore Processing : Dataproc (PySpark), Dataflow (batch & stream) Ingestion : Pub/Sub, Cloud Functions, Cloud Run Serving : Vertex AI Online Prediction, TF Serving, Kubernetes-based custom APIs, Cloud Run CI/CD & IaC : GitHub Actions, GitLab CI