30 Ai Engineer Jobs - Page 2

We are seeking a highly skilled AI Engineer with a mindset to design, build, intelligent systems for robust, scalable deployment. The ideal candidate will demonstrate proven experience in fine-tuning and optimizing AI products for real-world impact.

Dear Candidate Greetings from A2Z HR Consultants !!!!!!!! We are hiring for one of the renowned Web Software company based in Noida Number of working days: 5 Shift Timings: Day Shifts Salary: upto 32 LPA Profile: AI/ ML Engineer Experience Required: Min 5 Years **** Work from Office only Job Summary: Join our forward-thinking team to pioneer cutting-edge AI solutions that transform industries. We seek an AI Expert with 5+ years of experience in Python, machine learning, and large language models (LLMs), paired with robust MLOps expertise. You will architect, optimize, and deploy scalable AI systems, focusing on LLM fine-tuning (e.g., Llama, GPT, Mistral), Retrieval-Augmented Generation (RAG), and production-grade deployment on AWS . If you thrive on solving complex challenges, driving ethical AI innovation, and leading cross-functional teams, this role is for you. Key Responsibilities: Design, develop, and deploy AI/ML models using Python and relevant frameworks (TensorFlow, PyTorch, Scikit-learn, etc.). Optimize and fine-tune machine learning algorithms for performance, scalability, and accuracy. Work with large datasets to extract insights, preprocess data, and build predictive models. Develop and integrate AI-powered solutions into applications, including natural language processing (NLP), computer vision, and deep learning systems. Architect, fine-tune, and deploy large language models (LLMs) for various use cases such as chatbots, text generation, summarization, and document understanding. Implement retrieval-augmented generation (RAG) techniques to enhance LLM capabilities. Research and apply model compression techniques such as quantization and distillation to optimize LLM deployment. Leverage embeddings, knowledge graphs, and vector databases for efficient information retrieval and AI-driven insights. Develop robust MLOps/LLMOps pipelines for model versioning, monitoring, and CI/CD integration. Deploy AI models in cloud environments (GCP Vertex AI, AWS SageMaker, Azure ML) and optimize inference cost-performance trade-offs. Utilize containerization and orchestration tools such as Docker, Kubernetes, and Kubeflow for scalable AI deployments. Stay updated on the latest AI advancements and integrate emerging technologies into production systems. Ensure AI model interpretability, fairness, and adherence to ethical AI principles. Participate in code reviews, debugging, and troubleshooting of AI models and pipelines. Required Qualifications & Skills: Education: Bachelors or Masters degree in Computer Science, Artificial Intelligence, Data Science, or a related field. Experience: 5+ years of hands-on experience in AI, machine learning, or deep learning projects. Programming: Strong proficiency in Python and its AI/ML libraries (NumPy, Pandas, TensorFlow, PyTorch, Scikit-learn, etc.). LLM Expertise: Hands-on experience with LLMs such as OpenAIs GPT, Llama, Mistral, Gemini, PaLM, or similar frameworks. Fine-Tuning & Optimization: Experience fine-tuning LLMs, optimizing for cost-performance balance, and utilizing techniques like LoRA, PEFT, and RLHF. NLP & Deep Learning: Expertise in NLP model training, transformer-based architectures (BERT, T5, GPT, etc.), and model evaluation techniques. MLOps & LLMOps: Experience with model lifecycle management, monitoring, CI/CD pipelines, and cloud-based model deployment. Cloud & Deployment: Proficiency in deploying AI models on Google Cloud (Vertex AI), AWS, or Azure. Containerization & Orchestration: Experience with Docker, Kubernetes, and Kubeflow for AI model deployment. Data Engineering: Knowledge of data preprocessing, feature engineering, and handling large-scale datasets efficiently. Prompt Engineering: Strong understanding of prompt design, embedding generation, and model evaluation metrics for LLMs. Security & Ethics: Familiarity with AI security best practices, data privacy, and responsible AI principles. Interested candidates can reach out at 9711831492 or share your resume at gaurav.a2zhrconsultants@gmail.com Candidates who are already on Notice period or immediately available shall apply only. Regards Gaurav Kumar A2Z HR Consultants 9711831492

We are seeking seasoned GenAI Engineers with 5+ years of experience to design, deploy, and scale AI solutions. Candidates must be proficient in Python , with hands-on expertise in LLMs , RAG (Retrieval-Augmented Generation) , and frameworks like LangChain and Hugging Face . Experience working with Agentic AI or agent-based orchestration systems is highly desirable. Strong understanding of Computer Vision , including model development using OpenCV, YOLO, or similar libraries. Expertise in data manipulation libraries such as Pandas and NumPy is essential. Solid experience in FastAPI for building scalable APIs. Must have a good grasp of MLOps and ML Engineering , including model lifecycle management. Proven experience with DevOps tools like Docker, Kubernetes, Jenkins, and Terraform. Experience with CI/CD pipelines , automated testing, and Git version control is required. Ability to deploy AI models across cloud environments (AWS/GCP/Azure). Knowledge of transformer models, vector databases (e.g., FAISS, Pinecone), and prompt engineering. Hands-on experience with fine-tuning and optimizing large language models. Ability to work cross-functionally with data scientists, product managers, and software engineers. Strong communication skills with a problem-solving mindset. A passion for staying ahead in the rapidly evolving GenAI landscape.

About the Role: We are seeking a highly motivated and skilled Data Scientist with 25 years of industry experience in Machine Learning, Deep Learning, Natural Language Processing (NLP), and Generative AI (GenAI). The ideal candidate will have a strong foundation in Python programming and a passion for solving complex business problems using data. You will work on designing, developing, and deploying scalable AI solutions for real-world applications across domains. This is a hands-on role requiring technical expertise, experimentation, and collaboration across cross-functional teams. Key Responsibilities: Perform Exploratory Data Analysis (EDA) to uncover patterns, insights, and anomalies. Design, build, and optimize ML and DL models using Python for diverse business use cases. Develop and apply NLP techniques for text analysis and language understanding. Build, fine-tune, and deploy Generative AI solutions (LLMs, VLMs, RAG) tailored to enterprise needs. Create and maintain end-to-end ML pipelines , including data preprocessing, feature engineering, training, evaluation, and deployment. Manage and monitor models in production using MLOps workflows (CI/CD, model tracking, versioning, etc.). Stay updated with recent developments in AI/ML and continuously explore new tools and frameworks. Required Skills & Qualifications: Strong programming proficiency in Python is essential. Experience with Python libraries such as pandas, NumPy, scikit-learn, NLTK, Transformers (Hugging Face), TensorFlow/PyTorch . Solid understanding of machine learning algorithms, statistical modeling, and deep learning concepts. Hands-on experience in Natural Language Processing and Generative AI techniques (e.g., LLMs, RAG, VLMs, Agents). Experience with EDA, data preprocessing , and feature engineering. Familiarity with MLOps tools like MLflow, DVC or similar. Experience with Docker and containerized deployment of ML models. Working knowledge of databases both SQL and NoSQL . Experience deploying ML models in production environments . Familiarity with version control tools (e.g. Git) and collaborative workflows. Exposure to cloud platforms like AWS, GCP, or Azure is a plus.

Develop and implement the company’s IT strategy, aligning with business goals. Oversee the IT infrastructure, including networks, databases, and software systems. Ensure data security and compliance with relevant data protection regulations.