AI / Generative AI Engineer India 8 years Not disclosed Remote Full Time

AI / Generative AI Engineer Location: Remote ( Pan India ) Job Type: Fulltime NOTE: "Only immediate joiners or candidates with a notice period of 15 days or less will be considered" Overview: We are seeking a highly skilled and motivated AI/Generative AI Engineer to join our innovative team. The ideal candidate will have a strong background in designing, developing, and deploying artificial intelligence and machine learning models, with a specific focus on cutting-edge Generative AI technologies. This role requires hands-on experience with one or more major cloud platforms (Google Cloud Platform - GCP, Amazon Web Services - AWS) and/or modern data platforms (Databricks, Snowflake) . You will be instrumental in building and scaling AI solutions that drive business value and transform user experiences. Key Responsibilities: Design and Development: Design, build, train, and deploy scalable and robust AI/ML models, including traditional machine learning algorithms and advanced Generative AI models (e.g., Large Language Models - LLMs, diffusion models). Develop and implement algorithms for tasks such as natural language processing (NLP) , text generation, image synthesis, speech recognition, and forecasting. Work extensively with LLMs, including fine-tuning, prompt engineering, retrieval-augmented generation (RAG) , and evaluating their performance. Develop and manage data pipelines for data ingestion, preprocessing, feature engineering, and model training, ensuring data quality and integrity. Platform Expertise: Leverage cloud AI/ML services on GCP (e.g., Vertex AI, AutoML, BigQuery ML, Model Garden, Gemini) , AWS (e.g., SageMaker, Bedrock, S3 ), Databricks, and/or Snowflake to build and deploy solutions. Architect and implement AI solutions ensuring scalability, reliability, security, and cost-effectiveness on the chosen platform(s). Optimize data storage, processing, and model serving components within the cloud or data platform ecosystem. MLOps and Productionization: Implement MLOps best practices for model versioning, continuous integration/continuous deployment (CI/CD), monitoring, and lifecycle management. Deploy models into production environments and ensure their performance, scalability, and reliability. Monitor and optimize the performance of AI models in production, addressing issues related to accuracy, speed, and resource utilization. Collaboration and Innovation: Collaborate closely with data scientists, software engineers, product managers, and business stakeholders to understand requirements, define solutions, and integrate AI capabilities into applications and workflows. Stay current with the latest advancements in AI, Generative AI, machine learning, and relevant cloud/data platform technologies. Lead and participate in the ideation and prototyping of new AI applications and systems. Ensure AI solutions adhere to ethical standards, responsible AI principles, and regulatory requirements, addressing issues like data privacy, bias, and fairness. Documentation and Communication: Create and maintain comprehensive technical documentation for AI models, systems, and processes. Effectively communicate complex AI concepts and results to both technical and non-technical audiences. Required Qualifications: 8+ years of experience with software development in one or more programming languages, and with data structures/algorithms/Data Architecture. 3+ years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision). 3+ years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field. Proven experience as an AI Engineer, Machine Learning Engineer, or a similar role. Strong programming skills in Python. Familiarity with other languages like Java, Scala, or R is a plus. Solid understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning), deep learning concepts (e.g., CNNs, RNNs, Transformer s), and statistical modeling. Hands-on experience with developing and deploying Generative AI models and techniques, including working with Large Language Models ( LLMs like GPT, BERT, LLaMA, etc .). Proficiency in using common A I/ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, Keras, Hugging Face Transformers, LangChain, etc. Demonstrable experience with at least one of the following cloud/data platforms: GCP: Experience with Vertex AI, BigQuery ML, Google Cloud Storage, and other GCP AI/ML services . AWS: Experience with SageMaker, Bedrock, S3, and other AWS AI/ML services . Databricks: Experience building and scaling AI/ML solutions on the Databricks Lakehouse Platform , including MLflow. Snowflake: Experience leveraging Snowflake for data warehousing, data engineering for AI/ML workloads , and Snowpark. Experience with data engineering, including data acquisition, cleaning, transformation, and building ETL/ELT pipelines. Knowledge of MLOps tools and practices for model deployment, monitoring, and management. Familiarity with containerization technologies like Docker and orchestration tools like Kubernetes. Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. Show more Show less

Generative AI Engineer LLM/NLP Greater Kolkata Area 8 years None Not disclosed Remote Full Time

Role : AI / Generative AI Engineer Location : Remote ( Pan India ). Job Type : Fulltime. NOTE : "Only immediate joiners or candidates with a notice period of 15 days or less will be considered". Overview We are seeking a highly skilled and motivated AI/Generative AI Engineer to join our innovative team. The ideal candidate will have a strong background in designing, developing, and deploying artificial intelligence and machine learning models, with a specific focus on cutting-edge Generative AI technologies. This role requires hands-on experience with one or more major cloud platforms (Google Cloud Platform GCP, Amazon Web Services AWS) and/or modern data platforms (Databricks, Snowflake). You will be instrumental in building and scaling AI solutions that drive business value and transform user experiences. Key Responsibilities Design and Development : Design, build, train, and deploy scalable and robust AI/ML models, including traditional machine learning algorithms and advanced Generative AI models (e.g., Large Language Models LLMs, diffusion models). Develop and implement algorithms for tasks such as natural language processing (NLP), text generation, image synthesis, speech recognition, and forecasting. Work extensively with LLMs, including fine-tuning, prompt engineering, retrieval-augmented generation (RAG), and evaluating their performance. Develop and manage data pipelines for data ingestion, preprocessing, feature engineering, and model training, ensuring data quality and integrity. Platform Expertise Leverage cloud AI/ML services on GCP (e.g., Vertex AI, AutoML, BigQuery ML, Model Garden, Gemini), AWS (e.g., SageMaker, Bedrock, S3), Databricks, and/or Snowflake to build and deploy solutions. Architect and implement AI solutions ensuring scalability, reliability, security, and cost-effectiveness on the chosen platform(s). Optimize data storage, processing, and model serving components within the cloud or data platform ecosystem. MLOps And Productionization Implement MLOps best practices for model versioning, continuous integration/continuous deployment (CI/CD), monitoring, and lifecycle management. Deploy models into production environments and ensure their performance, scalability, and reliability. Monitor and optimize the performance of AI models in production, addressing issues related to accuracy, speed, and resource utilization. Collaboration And Innovation Collaborate closely with data scientists, software engineers, product managers, and business stakeholders to understand requirements, define solutions, and integrate AI capabilities into applications and workflows. Stay current with the latest advancements in AI, Generative AI, machine learning, and relevant cloud/data platform technologies. Lead and participate in the ideation and prototyping of new AI applications and systems. Ensure AI solutions adhere to ethical standards, responsible AI principles, and regulatory requirements, addressing issues like data privacy, bias, and fairness. Documentation And Communication Create and maintain comprehensive technical documentation for AI models, systems, and processes. Effectively communicate complex AI concepts and results to both technical and non-technical audiences. Required Qualifications 8+ years of experience with software development in one or more programming languages, and with data structures/algorithms/Data Architecture. 3+ years of experience with state of the art GenAI techniques (e.g., LLMs, Multi-Modal, Large Vision Models) or with GenAI-related concepts (language modeling, computer vision). 3+ years of experience with ML infrastructure (e.g., model deployment, model evaluation, optimization, data processing, debugging). Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related technical field. Proven experience as an AI Engineer, Machine Learning Engineer, or a similar role. Strong programming skills in Python. Familiarity with other languages like Java, Scala, or R is a plus. Solid understanding of machine learning algorithms (supervised, unsupervised, reinforcement learning), deep learning concepts (e.g., CNNs, RNNs, Transformers), and statistical modeling. Hands-on experience with developing and deploying Generative AI models and techniques, including working with Large Language Models (LLMs like GPT, BERT, LLaMA, etc.). Proficiency in using common AI/ML frameworks and libraries such as TensorFlow, PyTorch, scikit-learn, Keras, Hugging Face Transformers, LangChain, etc. Demonstrable experience with at least one of the following cloud/data platforms : GCP : Experience with Vertex AI, BigQuery ML, Google Cloud Storage, and other GCP AI/ML services. AWS : Experience with SageMaker, Bedrock, S3, and other AWS AI/ML services. Databricks : Experience building and scaling AI/ML solutions on the Databricks Lakehouse Platform, including MLflow. Snowflake : Experience leveraging Snowflake for data warehousing, data engineering for AI/ML workloads, and Snowpark. Experience with data engineering, including data acquisition, cleaning, transformation, and building ETL/ELT pipelines. Knowledge of MLOps tools and practices for model deployment, monitoring, and management. Familiarity with containerization technologies like Docker and orchestration tools like Kubernetes. Strong analytical and problem-solving skills. Excellent communication and collaboration abilities. (ref:hirist.tech)

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