10 Ai Developer Jobs

Job Description: AI Engineer (Junior / Associate Level) About the Role We are looking for a passionate and hands-on AI Developer with around 3 years of experience in building and deploying machine learning models and working with the latest AI tools and frameworks. You will be working closely with our data science and engineering teams to develop smart, scalable, and production-ready AI solutions. Key Responsibilities Design, develop, test, and deploy machine learning models for classification, regression, recommendation, or NLP tasks. Work with state-of-the-art AI tools and libraries such as LangChain, Hugging Face, OpenAI, LlamaIndex, etc. Integrate Large Language Models (LLMs) into applications via APIs or custom fine- tuning. Build data pipelines for training and inference, ensuring model performance and robustness. Collaborate with software developers, product managers, and data engineers to turn AI models into usable products. Stay updated with the latest research and innovations in the AI/ML space and bring new ideas into development. Optimize model performance and scale AI solutions for production. Required Skills & Experience Bachelor's or Master's degree in Computer Science, AI, Data Science, or a related field. 2 to 4 years of hands-on experience in machine learning and deep learning. Proficiency in Python and libraries like TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy. Good understanding of LLMs and experience working with platforms like OpenAI, Claude, Cohere, or Hugging Face Transformers. Experience in prompt engineering, fine-tuning, or using frameworks like LangChain, LlamaIndex, or Haystack. Exposure to cloud platforms (AWS, GCP, Azure) and tools like Docker, Git, CI/CD workflows. Strong analytical and problem-solving skills. Understanding of data preprocessing, feature engineering, and model evaluation techniques. Good to Have Experience building AI chatbots or virtual assistants. Knowledge of Vector databases (e.g., Chroma, Pinecone, Weaviate). Familiarity with MLOps tools like MLflow, Kubeflow, Vertex AI, etc. Experience with RESTful APIs or microservices architecture. Participation in Kaggle competitions or open-source AI projects. What We Offer Opportunity to work on real-world AI applications and products. A collaborative, learning-first environment. Exposure to enterprise-grade AI deployments and tools. Flexible working hours and remote work options. Competitive compensation and career growth path.

Role & responsibilities Minimum Qualifications: • 23 years of hands-on experience developing production-grade LLM applications. • 3 to 5 years of overall software development experience. • Minimum 1 year of experience in autonomous agent development using the LangGraph framework or similar. Must-Have Skills: 1. Production-Grade LLM Development: Proven experience in developing and deploying large language model applications. 2. Autonomous Agent Development: Hands-on experience with frameworks like LangGraph for building autonomous agents. 3. Advanced RAG Architectures: Expertise in hybrid search, multi-vector retrieval, and contextual compression. 4. Prompt Engineering and Vector Search Optimization: Proficiency in crafting effective prompts and optimizing vector searches. 5. Performance Tuning and Token Optimization: Strong understanding of response caching and techniques to optimize token usage. Nice-to-Have Skills: 1. Scalable Architectures: Experience with Kubernetes, Docker, and cloud platforms, preferably Azure. 2. NoSQL Databases: Familiarity with MongoDB and ElasticSearch. 3. API-Driven Development: Knowledge of building and maintaining APIs. 4. CI/CD Pipelines and Agile Methodologies: Experience with continuous integration/continuous deployment pipelines and Agile practices. 5. Analytical and Problem-Solving Skills: Strong analytical abilities

Use cases using LLM models and in-production Current LLM challenges and options to discuss Bring in a new use case approach using LLM models Dev methodology in AI use cases Cost optimization using various models Routing scenarios in using models

A Generative AI Developer needs skills in Python, TensorFlow, PyTorch, and deep learning. Expertise in GANs, VAEs, transformers, NLP, computer vision, and data preprocessing is crucial. Knowledge of cloud platforms

Role & responsibilities: Overall Experience : 10+ years of experience in the field of AI and ML ( Machine Learning ) Experience : 2 Years of relevant experience and previous work in any related AI is a plus ] Gen AI Experience Text embeddings Vector DB AI Frameworks [ Lang chain / Lama Index/ Language Graph* ] Prompt Engineering & Techniques RAG ( Retrieve Augment and Generate ) MCP ( Model Context Protocol )* Agentic AI* LLM ( Large Language Models , both open source and proprietary ) Measuring Model Accuracy* Deploying Models on-prem and on the cloud

Job Description: AI Developer with Azure and Java skills : Job Summary: We are seeking an experienced AI Developer with strong proficiency in Microsoft Azure cloud services and Java programming . In this role, you will design, develop, and deploy AI-powered applications and services, leveraging Azure AI tools such as Azure Cognitive Services, Azure Machine Learning, and related infrastructure. Your Java expertise will be key in integrating AI models into scalable applications and services. Key Responsibilities: Design and implement AI models using Azure AI services. Integrate AI capabilities into Java-based applications. Develop, test, and deploy cloud-native AI solutions on Azure. Collaborate with data scientists, architects, and DevOps teams to deliver end-to-end AI solutions. Optimize AI workloads for performance, scalability, and cost efficiency. Required Skills: Proficiency in Java (Spring Boot or other frameworks). Experience with Azure services (Cognitive Services, Azure ML, Azure Functions, etc.). Knowledge of machine learning concepts and libraries. Familiarity with RESTful APIs and cloud deployment pipelines. Strong problem-solving and collaboration skills. How to Apply: Interested candidates are encouraged to send their updated resume along with a cover letter highlighting their relevant experience to in_jobs@lucidtechinc.com . Please include the following details in your email: Current location Current CTC Expected CTC Notice Period Join Our Team! Be part of a dynamic organization dedicated to excellence in recruitment and client satisfaction. We look forward to welcoming you to Lucid Technologies Inc. Thanks & Regards, HR Department Lucid Technologies Inc. Email: in_jobs@lucidtechinc.com

5+ years of professional experience in software engineering, preferably in ML/AI environments Strong command of Python 3, including data science/ML libraries (Pandas, NumPy, Scikit-learn, proficient in React and OO languages like Java or C# Required Candidate profile Design and develop AI-powered agents to improve decision-making and automation in real world scenarios Build and implement agent workflows using LLMs and orchestration frameworks like LangGraph

1.JD:-Cloud AI Architect *Azure Expert *Intergration *Python *C# *Networking *Cybersecurity 2.JD:- AI Developer *Azure AI Resources *Promt *Engneering *Databricks *Datablake 3.JD:- Data Engineers *Python *Big Data *Data Pipelines *Intergrations 4.JD: - Full Stack Developer *React *.Net *NodeJS 5.JD: - Data Scientist Send your resume to veena.g_c@sandvik.com

Looking for candidate who are ready to join us within 30 days max. Required Skills & Qualifications: Experience: 5+ years in AI/ML development, with a proven track record of deploying scalable AI systems. Technical Expertise: Proficient in Python, TensorFlow, PyTorch, yolo and other AI/ML frameworks. Experience with cloud platforms (AWS, Google Cloud, Azure) for AI deployment. Familiar with containerization (Docker) and CI/CD pipelines. Strong skills in Git, version control, and debugging. Leadership experience, including code review.

Key Responsibilities: Build, train, and evaluate machine learning models using PyTorch, TensorFlow, or similar frameworks. Apply concepts like neural networks, regression algorithms, and loss functions in real-world projects. Participate in designing and implementing scalable AI solutions. Collaborate with cross-functional teams to integrate AI modules into products. Document your process, experiments, and learnings clearly. Explain model decisions and architecture to non-technical stakeholders, if needed.