AI-ML Engineer

Job Description

Job Responsibilities The Generative AI/ML Application Developer role will primarily focus on defining requirements and designing solutions for artificial intelligence and machine learning applications. This position will actively engage with project teams to strategize, establish, and execute comprehensive requirements gathering and management processes tailored for AI/ML projects. Required Technical Skills/Education: Knowledge of libraries such as TensorFlow, PyTorch, and scikit-learn is crucial for implementing AI and machine learning algorithms. Generative AI Models: Proven experience with generative models such as Generative Pretrained Models (GPTs), Generative Adversarial Networks (GANs) Variational Autoencoders (VAEs), and Diffusion Models. Libraries: Knowledge of libraries such as Langchain, Langgarph, Llamaindex, Autogen, openai etc is crucial for implementing machine learning algorithms and generative AI solutions. Prompt engineering Conversant with prompt engineering techniques like Chain of Thoughts, few-shot prompting, tree of thoughts, Reflections etc. Cloud Platforms: Familiarity with cloud platforms like AWS, Azure, or Google Cloud Platform. Generative AI Techniques: Experience with techniques such as image generation, text generation, and style transfer. Model Evaluation: Experience with model evaluation techniques such as Inception Score (IS), Frchet Inception Distance (FID), and BLEU score for generative models. TensorFlow and PyTorch is important for developing advanced GenAI models. Proficiency in building and training deep neural networks using these frameworks is highly desirable. Proficiency in data processing tools such as Pandas and NumPy for data manipulation, cleaning, and feature engineering Familiarity with cloud platforms like AWS, Azure, or Google Cloud Platform Strong understanding of various machine learning algorithms, including supervised learning, unsupervised learning GenAI and traditional machine learning projects and Knowledge of algorithm selection and tuning is crucial for achieving optimal model performance. Experience with model evaluation techniques such as cross-validation, hyperparameter tuning. Strong coding experience: python, PyTorch. Fluent English (both written and spoken), solid technical writing, presentation and communication skills. Preferred Technical Skills/Education: - Strong hands-on experience in Reinforcement Learning and machine learning - AWS Azure deployments - Good to have skill LLMs, Agent LLMs - Large Language Models: Familiarity with large language models (LLMs) such as GPT-3.4/4, Llama3.1 etc and their application int. - Generative AI Projects: Hands-on experience with implementing and deploying generative AI models in real-world applications. - Cloud Deployments: Experience with AWS and Azure deployments for machine learning workflows, including the use of services like AWS SageMaker and Azure Machine Learning.