5 Sklearn Jobs

Experience Required We are looking for an experienced AI Engineer to join our team. The ideal candidate will have a strong background in designing, deploying, and maintaining advanced AI/ML models with expertise in Natural Language Processing (NLP), Computer Vision, and architectures like Transformers and Diffusion Models. You will play a key role in developing AI-powered solutions, optimizing performance, and deploying and managing models in production environments. Key Responsibilities 1. AI Model Development and Optimization: Design, train, and fine-tune AI models for NLP, Computer Vision, and other domains using frameworks like TensorFlow and PyTorch. Work on advanced architectures, including Transformer-based models (e.g., BERT, GPT, T5) for NLP tasks and CNN-based models (e.g., YOLO, VGG, ResNet) for Computer Vision applications. Utilize techniques like PEFT (Parameter-Efficient Fine-Tuning) and SFT (Supervised Fine-Tuning) to optimize models for specific tasks. Build and train RLHF (Reinforcement Learning with Human Feedback) and RL-based models to align AI behavior with real-world objectives., Explore multimodal AI solutions combining text, vision, and audio using generative deep learning architectures. 2. Natural Language Processing (NLP): Develop and deploy NLP solutions, including language models, text generation, sentiment analysis, and text-to-speech systems. Leverage advanced Transformer architectures (e.g., BERT, GPT, T5) for NLP tasks. 3. AI Model Deployment and Frameworks: Deploy AI models using frameworks like VLLM, Docker, and MLFlow in production-grade environments. Create robust data pipelines for training, testing, and inference workflows. Implement CI/CD pipelines for seamless integration and deployment of AI solutions. 4. Production Environment Management: Deploy, monitor, and manage AI models in production, ensuring performance, reliability, and scalability. Set up monitoring systems using Prometheus to track metrics like latency, throughput, and model drift. 5. Data Engineering and Pipelines: Design and implement efficient data pipelines for preprocessing, cleaning, and transformation of large datasets. Integrate with cloud-based data storage and retrieval systems for seamless AI workflows. 6. Performance Monitoring and Optimization: Optimize AI model performance through hyperparameter tuning and algorithmic improvements. Monitor performance using tools like Prometheus, tracking key metrics (e.g., latency, accuracy, model drift, error rates etc.) 7. Solution Design and Architecture: Collaborate with cross-functional teams to understand business requirements and translate them into scalable, efficient AI/ML solutions. Design end-to-end AI systems, including data pipelines, model training workflows, and deployment architectures, ensuring alignment with business objectives and technical constraints. Conduct feasibility studies and proof-of-concepts (PoCs) for emerging technologies to evaluate their applicability to specific use cases. 8. Stakeholder Engagement: Act as the technical point of contact for AI/ML projects, managing expectations and aligning deliverables with timelines. Participate in workshops, demos, and client discussions to showcase AI capabilities and align solutions with client needs. Technical Skills Proficient in Python, with strong knowledge of libraries like NumPy, Pandas, SciPy, and Matplotlib for data manipulation and visualization. Expertise in TensorFlow, PyTorch, Scikit-learn, and Keras for building, training, and optimizing machine learning and deep learning models. Hands-on experience with Transformer libraries like Hugging Face Transformers, OpenAI APIs, and LangChain for NLP tasks. Practical knowledge of CNN architectures (e.g., YOLO, ResNet, VGG) and Vision Transformers (ViT) for Computer Vision applications. Proficiency in developing and deploying Diffusion Models like Stable Diffusion, SDX, and other generative AI frameworks. Experience with RLHF (Reinforcement Learning with Human Feedback) and reinforcement learning algorithms for optimizing AI behaviors. Proficiency with Docker and Kubernetes for containerization and orchestration of AI workflows. Hands-on experience with MLOps tools such as MLFlow for model tracking and CI/CD integration in AI pipelines. Expertise in setting up monitoring tools like Prometheus and Grafana to track model performance, latency, throughput, and drift. Knowledge of performance optimization techniques, such as quantization, pruning, and knowledge distillation, to improve model efficiency. Experience in building data pipelines for preprocessing, cleaning, and transforming large datasets using tools like Apache Airflow, Luigi Familiarity with cloud-based storage systems (e.g., AWS S3, Google BigQuery) for efficient data handling in AI workflows. Strong understanding of cloud platforms (AWS, GCP, Azure) for deploying and scaling AI solutions. Knowledge of advanced search technologies such as Elasticsearch for indexing and querying large datasets. Familiarity with edge deployment frameworks and optimization for resource-constrained environments.

Roles and Responsibilities: Collect and curate data based on specific project requirements. Perform data cleaning, preprocessing, and transformation for model readiness. Select and implement appropriate data models for various applications. Continuously improve model accuracy through iterative learning and feedback loops. Fine-tune large language models (LLMs) for applications such as code generation and data handling. Apply geometric deep learning techniques using PyTorch or TensorFlow. Essential Requirements: Strong proficiency in Python, with experience in writing efficient and clean code. Ability to process and transform natural language data for NLP applications. Solid understanding of modern NLP techniques such as Transformers, Word2Vec, BERT, etc. Strong foundation in mathematics and statistics relevant to machine learning and deep learning. Hands-on experience with Python libraries including NumPy, Pandas, SciPy, Scikit-learn, NLTK, etc. Experience in various data visualization techniques using Python or other tools. Working knowledge of DBMS and fundamental data structures. Familiarity with a variety of ML and optimization algorithms.

The Team : As a member of the Data Transformation - Cognitive Engineering team you will work on building and deploying ML powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for S&P Global Market Intelligence and our clients. You will spearhead deployment of AI products and pipelines while leading-by-example in a highly engaging work environment. You will work in a (truly) global team and encouraged for thoughtful risk-taking and self-initiative. Whats in it for you: Be a part of a global company and build solutions at enterprise scale Lead a highly skilled and technically strong team (including leadership) Contribute to solving high complexity, high impact problems Build production ready pipelines from ideation to deployment Responsibilities: Design, Develop and Deploy ML powered products and pipelines Mentor a team of Senior and Junior data scientists ML Engineers in delivering large scale projects Play a central role in all stages of the AI product development life cycle, including: Designing Machine Learning systems and model scaling strategies Research & Implement ML and Deep learning algorithms for production Run necessary ML tests and benchmarks for model validation Fine-tune, retrain and scale existing model deployments Extend existing ML librarys and write packages for reproducing components Partner with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirements Interpret results and present them to business leaders Manage production pipelines for enterprise scale projects Perform code reviews & optimization for your projects and team Lead and mentor by example, including project scrums Technical Requirements: Proven track record as a senior lead ML engineer Expert proficiency in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.) Excellent exposure to large scale model deployment strategies and tools Excellent knowledge of ML & Deep Learning domain Solid exposure to Information Retrieval, Web scraping and Data Extraction at scale Exposure to the following technologies - R-Shiny/Dash/Streamlit, SQL, Docker, Airflow, Redis, Celery, Flask/Django/FastAPI, PySpark, Scrapy Experience with SOTA models related to NLP and expertise in text matching techniques, including sentence transformers, word embeddings, and similarity measures Open to learning new technologies and programming languages as required A Masters PhD from a recognized institute in a relevant specialization Good to have: 6-7+ years of relevant experience in ML Engineering Prior substantial experience from the Economics/Financial industry Prior work to show on Github, Kaggle, StackOverflow etc.

About the Role: Grade Level (for internal use): 09 The Team: As a member of the Data Transformation team you will work on building ML powered products and capabilities to power natural language understanding, data extraction, information retrieval and data sourcing solutions for S&P Global Market Intelligence and our clients. You will spearhead development of production-ready AI products and pipelines while leading-by-example in a highly engaging work environment. You will work in a (truly) global team and encouraged for thoughtful risk-taking and self-initiative. The Impact: The Data Transformation team has already delivered breakthrough products and significant business value over the last 3 years. In this role you will be developing our next generation of new products while enhancing existing ones aiming at solving high-impact business problems. Whats in it for you: Be a part of a global company and build solutions at enterprise scale Collaborate with a highly skilled and technically strong team Contribute to solving high complexity, high impact problems Key Responsibilities Design, Develop and Deploy ML powered products and pipelines Play a central role in all stages of the data science project life cycle, including: Identification of suitable data science project opportunities Partnering with business leaders, domain experts, and end-users to gain business understanding, data understanding, and collect requirements Evaluation/interpretation of results and presentation to business leaders Performing exploratory data analysis, proof-of-concept modelling, model benchmarking and setup model validation experiments Training large models both for experimentation and production Develop production ready pipelines for enterprise scale projects Perform code reviews & optimization for your projects and team Spearhead deployment and model scaling strategies Stakeholder management and representing the team in front of our leadership Leading and mentoring by example including project scrums What Were Looking For: 2+ years of professional experience in Data Science domain Expertise in Python (Numpy, Pandas, Spacy, Sklearn, Pytorch/TF2, HuggingFace etc.) Experience with SOTA models related to NLP and expertise in text matching techniques, including sentence transformers, word embeddings, and similarity measures Expertise in probabilistic machine learning model for classification, regression & clustering Strong experience in feature engineering, data preprocessing, and building machine learning models for large datasets. Exposure to Information Retrieval, Web scraping and Data Extraction at scale OOP Design patterns, Test-Driven Development and Enterprise System design SQL (any variant, bonus if this is a big data variant) Linux OS (e.g. bash toolset and other utilities) Version control system experience with Git, GitHub, or Azure DevOps. Problem-solving and debugging skills Software craftsmanship, adherence to Agile principles and taking pride in writing good code Techniques to communicate change to non-technical people Nice to have Prior work to show on Github, Kaggle, StackOverflow etc. Cloud expertise (AWS and GCP preferably) Expertise in deploying machine learning models in cloud environments Familiarity in working with LLMs

Python Programming: Proficiency in Python, Working with libraries like TensorFlow, PyTorch,Transformers,NLTK,Pandas,sklearn, and other related libraries used in NLP tasks and fine tuning language models. Experience with building comprehensive python modules for NLP tasks like tokenization, word embeddings,classifications. Evaluating and selecting the open source and/or commercial LLMs suitable for financial lending domains Location: Delhi NCR,Bangalore,Chennai,Pune,Kolkata,Ahmedabad,Mumbai,Hyderabad