2 Aws Sagemaker. Jobs

Model Development: Research, design, and develop state-of-the-art generative models such as GPT, GANs, VAEs, or diffusion models for tasks like text generation, summarization, reasoning, Q&A, or predictive analytics. Fine-Tuning : Fine-tune pre-trained models (e.g., Llama3, Mistral, OpenAI GPT, BERT, T5) to meet domain-specific requirements. Data Preparation: Clean, preprocess, and structure large datasets to train, validate, and test generative AI models. Deployment: Implement scalable solutions for deploying generative AI models in production environments using tools like Docker, Kubernetes, or cloud platforms (AWS, GCP, Azure). Collaboration : Work closely with cross-functional teams, including product managers, engineers, and stakeholders, to align AI solutions with business goals. Evaluation: Develop metrics and benchmarks to evaluate model performance and ensure quality outputs. Research: Stay up to date with the latest advancements in AI/ML, particularly in generative AI techniques, and propose innovative solutions. Ethical AI Practices: Ensure ethical considerations are addressed in model development, including fairness, accountability, and explainability. Experience of architecting AI systems to solve complex business problems. Build advanced RAG pipelines, text chunking, and retrieval, LLM Prompt Engineering, using Vector Databases. Implement right LLM selection based on use cases and client criteria (GPT-4, Llama2, Mistral, Claude, Gemini, Flan, BERT) while managing trifecta of accuracy, cost, and latency/scale. Develop, fine-tune, context tune and implement state-of-the-art NLP models including Large Language Models like GPT, Llama3.1, Claude, BLOOM, Flan-T5, Falcon etc. fine-tuning PEFT LoRa/QLoRa adapters design AI systems & architectures, while considering good Responsible AI standards and AI Governance. Build Agentic AI, AutoGen, Muti-agents use cases, AI Autonomous Agents and LLM orchestration architectures for enterprise data at scale in Python. Hands on experience with complementary technologies around LLMs like embedders, vector databases (chroma, weaviate etc.), orchestration tools like Langchain, LlamaIndex etc. Conduct research and experimentation to improve existing models and propose novel approaches. Collaborate with cross-functional teams to integrate generative AI solutions into real-world applications. Stay up-to-date with the latest advancements in deep learning and generative models and apply them to enhance our AI capabilities. Document research findings, prepare technical reports, and contribute to whitepaper/scientific publications. Provide deep leadership and coaching in the project delivery lifecycle. Focus on shared learning, continuous improvement, and drive adoption of best practices. Qualifications Education and Experience: Masters of Science or PhD in computer science, data science, statistics, Natural Language Processing 2-3 years of experience in GenAI and Large Language Models. Strong programming skills in Python, including experience with libraries like TensorFlow, PyTorch, Hugging Face Transformers, or similar. as evidenced by released code (e.g., GitHub repositories version control awareness). Solid understanding of optimization techniques for training deep neural networks, regularization methods, and hyperparameter/fine tuning. Hands-on experience with generative AI models, such as Llama3, GPT4, Claude Knowledge of NLP, computer vision, or multimodal AI techniques. Proficiency in data manipulation and analysis tools (e.g., Pandas, NumPy, SQL). Experience in Generative AI Models and LLMs, finetuning LLMs, prompt engineering and experience with LLM orchestration frameworks like Langchain, LlamaIndex, RAGAS, etc. Strong software engineering skills for rapid and accurate development of AI models and systems. Understanding of Agentic AI, Autonomous Agents, AI Agents, AutoGen, Crew.ai, Langchain, and workflow steps design. NVIDIA Blueprints. Google AI Agents. Provide business-oriented solution with ability to communicate effectively, both verbally and in writing, with technical and non-technical stakeholders. Experience working in a collaborative environment, contributing to multidisciplinary teams and projects. Proven ability to solve complex problems, think creatively, and adapt to evolving research trends. Experience in deploying ML models in cloud environments (AWS SageMaker, GCP AI Platform, or Azure ML). Strong problem-solving and analytical skills. Excellent communication skills, both technical and non-technical. Skills and Competencies: 5 years of experience in AI, NLP including transformer architecture and LLMs, Computer Vision and related technologies Excellent communication and problem-solving skills. Working with cross-functional teams across different stakeholders. Ability to explain GenAI to non-technical audiences across many different industries. Ability to do GenAI Architecture solutioning for existing clients and potential clients. People leadership skills. Candidate is also hands on developer while people managing Data Scientists, ML Engineers etc. Experience in ML Engineering and MLOps, MLFlow Strong understanding of statistical and machine learning concepts Experience with deep learning frameworks such as TensorFlow and PyTorch Familiarity with key concepts and techniques used in generative models, such as variational autoencoders (VAEs), generative adversarial networks (GANs), and flow-based models. Strong programming skills in languages such as Python, along with experience working with popular deep learning frameworks like PyTorch and TensorFlow. Understanding of Graph Database and/or Vector Database along with knowledge of cloud services (e.g., AWS, Azure, GCP). Experience with deploying AI models in production environments. Familiarity with domain-specific applications of generative AI Leveraged both Azure and AWS for model inferencing. For finetuning, he has worked more on AWS SageMaker. Has used different retrieval, reranking, and generation techniques based on the use case complexity and the metrics required. Has played multiple roles in the past, such as an individual contributor, a solution architect, a mentor, a trainer, and a people manager. He also participates in RFI, RFP, thought leadership, and business development activities

Support investment portfolio reporting and quantitative analyses for a leading alternative asset manager. Develop and improve workflow efficiency through automation. Assist with development, calibration, prototyping, and documentation of risk models. Assist with preparation of materials for client and senior management. Assist with development of a robust risk management framework and analytics across portfolios. Analyze time series data to identify and report any trends or errors/exceptions, using quantitative techniques and machine learning models. Help foster a culture of risk awareness in partnership with other stakeholders. Keep up to date with topical issues in risk management. Key Skills: Experienced in data science, machine learning, and quantitative modeling. Proficient in programming languages like Python, SQL, and libraries like Pandas, Numpy, Matplotlib. Experienced in AWS/Amazon SageMaker and frameworks like TensorFlow. Has a commanding grasp on Excel and basic knowledge of BI tools like Tableau/Sigma Computing. Has experience with risk analytic platforms (e.g., FactSet, Risk Metrics, Bloomberg). Quantitative background such as Mathematics, Mathematical Finance, Econometrics, Data Science, and Statistics. Understanding of different alternative asset classes and instruments, with a strong knowledge of fixed income securities and their characteristics. Preferred qualification: CFA, FRM, CQF, or a PG degree in finance. Ability to work independently and thrive in a team-oriented environment. Comfortable taking initiative and being resourceful. Rich experience working in an investment risk, data science-related role preferred.