The Senior AI Engineer will design, develop, and deploy AI models and Generative AI solutions for real-world business applications. The role requires strong hands-on expertise in model development, fine‑tuning, and integrating AI systems into production environments.
Design, build, and fine‑tune machine learning, deep learning, and Generative AI models.
Develop Agentic AI systems using frameworks such as ADK, LangGraph, or similar.
Build solutions using LLMs, RAG pipelines, embeddings, and vector databases.
Implement AI systems for text, structured/unstructured data, and conversational use cases.
Apply 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 using APIs, event‑driven architectures, and cloud services.
Deploy AI workloads using Docker, Kubernetes, and platforms like Azure ML, AWS SageMaker, or GCP Vertex AI.
Optimize AI solutions for performance, cost, latency, and security.
Collect, preprocess, and manage datasets for training and inference.
Monitor and optimize models and prompts for accuracy and scalability.
Collaborate with Solution Architects and business teams on production‑ready AI solutions.
Promote Responsible AI practices including fairness and bias mitigation.
Proficiency in Python, SQL, TensorFlow, and PyTorch.
Strong understanding of Generative AI, transformers, embeddings, and fine‑tuning.
Experience with Agentic AI frameworks (ADK, LangGraph, AutoGen).
Knowledge of AWS, Azure, or GCP for AI deployment.
Experience with prompt engineering, vector databases (Pinecone, Weaviate), and RAG.
Hands‑on experience with cloud‑native AI deployments.
Familiarity with APIs, microservices, and event‑driven architectures.
Bachelor’s or master’s degree in computer science, AI, Data Science, or related field.
Hands‑on experience with LLMs and Generative AI applications.
Strong communication and interpersonal skills.