Data Dynamics Artificial Intelligence/Machine Learning Engineer/Architect Python

Job description

<p><b>Description : </b><br/><br/>AI/ML Developer/Architect<br/><br/>Location : Pune (Onsite)<br/><br/>Experience : 7+ years<br/><br/>Role : AI/ML Developer/Architect<br/><br/><b>Job Summary : </b><br/><br/>We are looking for a highly skilled AI/ML Developer/Architect with 7+ years of experience in designing, developing, and deploying Machine Learning (ML) and Artificial Intelligence (AI) solutions.<br/><br/>The ideal candidate should be proficient in Python, TensorFlow, PyTorch, and cloud platforms (AWS, Azure, GCP) and should have hands-on experience in building end-to-end AI/ML models, MLOps pipelines, and scalable AI architectures.<br/><br/><b>Key Responsibilities : </b><br/><br/><b>AI/ML Development : </b><br/><br/>- Design, develop, and optimize ML/DL models for real-world applications across multiple industries and use cases.<br/><br/>- Collaborate with data scientists, engineers, and stakeholders to define model requirements and success metrics.<br/><br/>- Implement, test, and deploy AI models using frameworks like TensorFlow, PyTorch, or Scikit-learn to solve complex business problems.<br/><br/>- Develop reusable model components to accelerate development and experimentation cycles.<br/><br/>- Fine-tune models for accuracy, performance, and efficiency through hyperparameter optimization and architecture experimentation.<br/><br/>- Perform regular model evaluations to assess bias, drift, and robustness to ensure fairness and reliability.<br/><br/><b>MLOps & Deployment : </b><br/><br/>- Build scalable ML pipelines and deploy models using Docker, Kubernetes, and cloud services (AWS/GCP/Azure) for both batch and real-time applications.<br/><br/>- Establish automated CI/CD pipelines for model versioning, testing, and deployment using MLflow, Kubeflow, or SageMaker.<br/><br/>- Implement model monitoring, logging, and alerting to ensure continuous model performance and health checks post-deployment.<br/><br/>- Optimize AI solutions for low-latency and high-availability performance under varying workloads.<br/><br/>- Implement infrastructure as code (IaC) practices to maintain and deploy AI/ML infrastructure in a repeatable manner.<br/><br/>- Work with cross-functional teams to ensure that data security, compliance, and privacy policies are integrated into MLOps pipelines.<br/><br/><b>AI Architecture & Design : </b><br/><br/>- Architect end-to-end AI/ML solutions, including data ingestion, preprocessing, feature engineering, training, and inference pipelines.<br/><br/>- Define scalable, modular, and cost-effective AI architectures that align with enterprise goals and technology stacks.<br/><br/>- Design solutions to support both on-premise and cloud-based AI workflows for flexibility and scalability.<br/><br/>- Create reusable design patterns for AI model integration with existing enterprise systems, APIs, and databases.<br/><br/>- Implement best practices for model governance, including compliance with regulatory standards, auditability, and explainability.<br/><br/>- Work with business leaders to translate strategic objectives into AI-driven initiatives and roadmaps.<br/><br/><b>Data Engineering & Processing : </b><br/><br/>- Work with large, complex datasets to optimize ETL pipelines for AI model training and inference.<br/><br/>- Design and build scalable data pipelines using distributed processing frameworks like Spark, Hadoop, or Dask.<br/><br/>- Collaborate with data engineering teams to enhance data accessibility, quality, and reliability for machine learning workflows.<br/><br/>- Leverage SQL/NoSQL databases and data lakes to create data schemas and structures that support efficient ML operations.<br/><br/></p><p>- Implement feature stores and data cataloging tools to streamline feature reuse and data discovery across teams.<br/><br/>- Develop and maintain data governance frameworks to ensure data security, privacy, and compliance.<br/><br/><b>Research & Innovation : </b><br/><br/>- Stay updated with cutting-edge AI research and trends, including advancements in Generative AI, NLP, and Computer Vision technologies.<br/><br/>- Experiment with LLMs (Large Language Models) and generative AI models (e.g., GPT, Stable Diffusion) to develop innovative AI solutions.<br/><br/>- Prototype and evaluate emerging AI technologies to assess their applicability to business problems.<br/><br/>- Contribute to open-source AI/ML projects, research papers, and industry conferences to establish thought leadership.<br/><br/>- Collaborate with universities, research institutes, and external partners to foster innovation and access new AI capabilities.<br/><br/>- Identify opportunities to apply AI in unexplored areas to create competitive advantages for the organization.<br/><br/><b>Required Skills & Qualifications : </b><br/><br/><b>Programming & AI Frameworks : </b><br/><br/>- Proficiency in Python and key AI libraries such as TensorFlow, PyTorch, and Keras, with experience in both supervised and unsupervised learning models.<br/><br/>- Experience working with computer vision libraries like OpenCV and other image/video processing frameworks.<br/><br/>- Deep expertise in natural language processing (NLP) techniques using Transformers, BERT, GPT, and related models.<br/><br/>- Strong understanding of ML algorithms, deep learning architectures (CNNs, RNNs, LSTMs), and optimization techniques (e.g., gradient descent, hyperparameter tuning).<br/><br/>- Proficiency in at least one secondary programming language (e.g., Java, C++, or Go) to support AI integration into legacy systems.<br/><br/>- Experience with tools for model evaluation, visualization, and performance monitoring such as TensorBoard and ML visualization dashboards.<br/><br/><b>MLOps & Deployment : </b><br/><br/>- Hands-on experience with Docker for containerization and Kubernetes for orchestration of scalable AI model deployments.<br/><br/>- Familiarity with web frameworks like FastAPI and Flask for serving AI models and building RESTful APIs.<br/><br/>- Experience with cloud-based ML services such as AWS SageMaker, GCP Vertex AI, or Azure ML, including managing pipelines and infrastructure automation.<br/><br/>- Expertise in using MLOps tools like MLflow, Kubeflow, or Argo Workflows for model tracking, lifecycle management, and version control.<br/><br/>- Knowledge of serverless architecture and microservices deployment strategies to optimize cloud infrastructure costs and performance.<br/><br/>- Ability to implement monitoring, logging, and auto-scaling for AI models in production environments</p> (ref:hirist.tech)

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