2 Sbert Jobs

Prismforce is a Vertical SaaS company at the forefront of revolutionizing the Talent Supply Chain for global Technology, R&D/Engineering, and IT Services companies. Through our AI-powered product suite, we aim to enhance business performance by facilitating operational flexibility, accelerating decision-making processes, and boosting profitability. Our ultimate mission is to establish ourselves as the premier industry cloud/SaaS platform for tech services and talent organizations worldwide. We are currently seeking a highly experienced Data Scientist to join our team in Bangalore within the AI & ML department. In this role, you will be instrumental in leveraging your expertise in Natural Language Processing (NLP) and machine learning to develop customized AI solutions that address real-world talent intelligence challenges. The scope of your responsibilities will encompass a wide range of tasks, from data exploration and model development to evaluation and deployment, all while emphasizing the creation of robust, scalable systems utilizing cutting-edge techniques. As a Data Scientist at Prismforce, your key responsibilities will include: - Developing and fine-tuning models for Named Entity Recognition (NER), text classification, semantic similarity, and clustering - Utilizing pretrained embeddings, transformers, and Large Language Models (LLMs) to extract and standardize structured data from unstructured text - Designing and conducting experiments to assess model performance and drive enhancements - Collaborating closely with product and engineering teams to translate models into reliable, production-ready solutions - Continuously researching and exploring open-source tools to enhance performance and scalability The ideal candidate for this position should possess the following qualifications: - A minimum of 13 years of hands-on experience in applied NLP and ML - Proficiency in Python programming, along with familiarity with Pandas, Scikit-learn, PyTorch, or TensorFlow - Knowledge of modern NLP toolkits such as Spacy, HuggingFace Transformers, SBERT, among others - A solid grasp of embedding models, attention mechanisms, and evaluation metrics - The ability to deconstruct abstract problems and develop tailored, data-driven solutions Additionally, the following skills and experiences would be considered advantageous: - Prior exposure to LLM APIs, prompt engineering, or metadata generation using LLMs - Familiarity with knowledge graphs, taxonomy design, or vector databases - Contributions to open-source NLP tools or involvement in research projects within the field If you are a seasoned Data Scientist with a passion for NLP and ML, and you are eager to make a significant impact within our innovative team, we encourage you to apply and become a part of Prismforce's mission to reshape the future of talent intelligence.,

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.