You are an AI engineer responsible for designing, developing, and deploying AI models and solutions to address real-world business challenges. Your role will involve hands-on coding, model training, and integration into production systems.

**Key Responsibilities:**
- Design, build, and fine-tune machine learning, deep learning, and generative AI models for various real-world use cases.
- Develop agentic AI systems utilizing frameworks like ADK, LangGraph, or equivalent tools for orchestration and reasoning.
- Create solutions leveraging LLMs, RAG pipelines, embeddings, and vector databases.
- Design and implement AI systems for text, structured/unstructured data, and conversational use cases.
- Apply core NLP techniques such as classification, summarization, entity recognition, and semantic search.
- Develop scalable algorithms for search, retrieval, and real-time inference.
- Build and integrate AI microservices into enterprise systems using APIs, event-driven architectures, and cloud services.
- Deploy and manage AI workloads using Docker, Kubernetes, and managed cloud AI platforms like Azure ML, AWS SageMaker, GCP Vertex AI.
- Optimize solutions for performance, cost, latency, and security.
- Collect, preprocess, and manage large datasets for fine-tuning and inference.
- Continuously monitor and optimize models and prompts for accuracy, scalability, and efficiency.
- Collaborate with solution architects and business teams to translate requirements into production-ready AI solutions.
- Advocate for Responsible AI practices, including fairness, bias mitigation, and compliance.

**Qualifications:**
- Proficiency in Python, SQL, and ML frameworks such as TensorFlow and PyTorch.
- Strong understanding of generative AI concepts including transformers, embeddings, and fine-tuning.
- Experience with Agentic AI frameworks like ADK, LangGraph, AutoGen.
- Familiarity with cloud platforms (AWS, Azure, GCP) for AI deployment.
- Knowledge of prompt engineering, vector databases like Pinecone and Weaviate, and retrieval-augmented generation (RAG).
- Hands-on experience with cloud-native AI deployments on Azure, AWS, and GCP.
- Understanding of API-driven design, microservices, and event-driven architectures.

**Additional Details:**
The company emphasizes the importance of Responsible AI practices, including fairness, bias mitigation, and compliance in their AI solutions development.

(Note: Company details have not been provided in the job description) You are an AI engineer responsible for designing, developing, and deploying AI models and solutions to address real-world business challenges. Your role will involve hands-on coding, model training, and integration into production systems.

**Key Responsibilities:**
- Design, build, and fine-tune machine learning, deep learning, and generative AI models for various real-world use cases.
- Develop agentic AI systems utilizing frameworks like ADK, LangGraph, or equivalent tools for orchestration and reasoning.
- Create solutions leveraging LLMs, RAG pipelines, embeddings, and vector databases.
- Design and implement AI systems for text, structured/unstructured data, and conversational use cases.
- Apply core NLP techniques such as classification, summarization, entity recognition, and semantic search.
- Develop scalable algorithms for search, retrieval, and real-time inference.
- Build and integrate AI microservices into enterprise systems using APIs, event-driven architectures, and cloud services.
- Deploy and manage AI workloads using Docker, Kubernetes, and managed cloud AI platforms like Azure ML, AWS SageMaker, GCP Vertex AI.
- Optimize solutions for performance, cost, latency, and security.
- Collect, preprocess, and manage large datasets for fine-tuning and inference.
- Continuously monitor and optimize models and prompts for accuracy, scalability, and efficiency.
- Collaborate with solution architects and business teams to translate requirements into production-ready AI solutions.
- Advocate for Responsible AI practices, including fairness, bias mitigation, and compliance.

**Qualifications:**
- Proficiency in Python, SQL, and ML frameworks such as TensorFlow and PyTorch.
- Strong understanding of generative AI concepts including transformers, embeddings, and fine-tuning.
- Experience with Agentic AI frameworks like ADK, LangGraph, AutoGen.
- Familiarity with cloud platforms (AWS, Azure, GCP) for AI deployment.
- Knowledge of prompt engineering, vector databases like Pinecone and Weaviate, and retrieval-augmented generation (RAG).
- Hands-on experience with cloud-native AI deployments on Azure, AWS, and GCP.
- Understanding of API-driven design, microservices, and event-driven architectures.

Artificial Intelligence Engineer - Machine Learning Frameworks