Artificial Intelligence/Machine Learning Engineer

2 weeks ago

Hyderabad, Telangana, India People Equations Full time

Technical Knowledge:

  • Familiarity with a wide range of common software, programming languages, packages, libraries, tools and techniques found in the AI/ML stack and in Cloud/DevOps/MLOps.
  • Knowledge of the following is preferred: Python AI/ML Stack: PyTorch, NLTK, SciPy, Scikit-Learn, Numpy, Pandas, OpenCV or equivalent for image preprocessing
  • Familiarity with GenAI common solutions: Vertex AI, Open AI API, Llama index, Langchain, Stable Diffusion, Midjourney, etc
  • SQL/NoSql databases and queries
  • GCP (preferred), AWS, Azure
  • One or more ML toolkits or Python frameworks
  • Deep Learning concepts
  • MLOps best practices and concepts
  • Terraform, Kubernetes and Docker for deployment
  • Proper implementation of machine learning algorithms. Best selection criteria of suitable model such as decision tree, neural net, or an ensemble of multiple models
  • Probability and statistics and machine learning concepts such as precision, recall, optimization, hyperparameter tuning, overfitting, and interpretability
  • Model development, model serving and training/fine-tuning techniques.
  • Prompt engineering techniques in developing Instruction based LLMs. Familiarity with current concepts to fine tune AI models: LoRA, QLoRA, Dreambooth, Control Net, etc.
  • Design, and implementation of state-of-the-art generative models for natural language processing (NLP) tasks such as, text generation, text completion, language translation, and document summarization.
  • Design and implementation of chatBots or text processing tools powered by LLMs both commercial and open source.
  • Use of RAG to improve LLM solutions.
  • Work with SAs and collaborate with cross-functional teams to identify business requirements and deliver solutions that meet the customer needs.
  • Passionate to learn and stay updated with the latest advancements in generative AI and LLM.
  • Evaluate and preprocess large-scale datasets from structured or unstructured data (e.g. documents, images), ensuring data quality and integrity, and develop data pipelines for training and evaluation of generative models.
  • Ability to articulate to business stakeholders on the hallucination effects and various model behavioral analysis techniques followed.
  • Exposure to developing Guardrails for LLMs both with open source and cloud native models
Insight on your impact:
  • Conceptualize, Prototype, and Implement AI Solutions: Develop and deploy advanced generative and augmented AI solutions using LLM, NLP, image processing, and deep learning techniques using Google tools found in GCP and Vertex AI.
  • Technical Expertise: Utilize a wide range of AI/ML tools and frameworks to solve diverse client requirements. Understand each client's particular needs and how to translate their problems into a technical solution. Help clients with your opinion on best practices, future paths and technical designs for their current and your proposed solution.
  • Stakeholder Communication: Articulate complex AI concepts to business stakeholders, including hallucination effects and model behavioral analysis techniques.
  • Understand client analytics & modeling needs: build validated data pipelines to extract internal and external data, conduct exploratory analysis.
  • Database Management: Work with SQL/NoSQL and unstructured data to ensure dataset quality and integrity for technical solutions. Give recommendations of dataset management best practices.
  • Cloud expertise: Use technical knowledge and communication skills to understand, design and construct cloud solutions to each client's needs. Always aiming for efficient deployment and scaling of AI models.
  • Model Development & Optimization: select suitable models based on performance and client criteria, and optimize hyperparameters to ensure optimal performance. Educate and give informed opinion on how to track or log model versions, experiments, metrics and hyperparameters.
  • MLOps & DevOps: Implement and communicate MLOps best practices and collaborate with cross-functional teams to deliver scalable and reliable AI/ML solutions.
  • Prompt Engineering and fine tuning: Use advanced prompt engineering techniques to develop instruction-based LLMs. Apply fine tuning methods on AI models such as LoRA, QLoRA, Dreambooth, Control Net, etc.
  • LLMs, Chatbots & Text Processing: Develop chatbots and text processing tools powered by LLMs, both commercial and open source, and leverage RAG to enhance LLM solutions. Design and implement state-of-the-art generative models for NLP tasks, such as text generation, language translation, and document summarization. Develop guardrails for LLMs, ensuring compliance with ethical and regulatory standards for AI/ML models. Communicate LLMOps standards and recommendations.
  • Demonstrate engineering best practices: source control, time tracking, documentation, sharing retrospectives.
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