Artificial Intelligence Engineer - Machine Learning Frameworks

Job Description :

An AI engineer designs, develops, and deploys AI models and solutions, including generative AI applications, to solve real-world business problems. This role involves hands-on coding, model training, and integration into production systems.

- Design, build, and fine-tune machine learning, deep learning, and generative AI models for real-world use cases.

- Develop agentic AI systems using frameworks such as ADK, LangGraph, or equivalents for orchestration and reasoning.

- Build 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, including classification, summarisation, 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 operate AI workloads using Docker, Kubernetes, and managed cloud AI platforms (Azure ML, AWS SageMaker, GCP Vertex AI).

- Optimise solutions for performance, cost, latency, and security.

- Collect, preprocess, and manage large datasets for fine-tuning and inference.

- Continuously monitor and optimise models and prompts for accuracy, scalability, and efficiency.

- Partner with solution architects and business teams to translate requirements into production-ready AI solutions.

- Champion Responsible AI practices, including fairness, bias mitigation, and compliance.

- Proficiency in Python, SQL, and ML frameworks (TensorFlow, PyTorch).

- Strong understanding of generative AI concepts (transformers, embeddings, fine-tuning).

- Experience with Agentic AI frameworks (ADK, LangGraph, AutoGen).

- Familiarity with cloud platforms (AWS, Azure, GCP) for AI deployment.

- Knowledge of prompt engineering, vector databases (Pinecone, Weaviate), and retrieval-augmented generation (RAG).

- Hands-on with cloud-native AI deployments (Azure, AWS, GCP).

- Familiarity with API-driven design, microservices, and event-driven architectures.

- Bachelor's/Master's in Computer Science, AI, Data Science, or a related field.

- 3-9 years of experience in AI/ML development and deployment.

- Hands-on experience with LLMs and generative AI applications.