AI/ML Engineer (Intern)

We are seeking a skilled individual to join our team as an AI/ML Engineer (Intern).

Position: AI/ML Engineer (Intern)

Type: Internship

Duration: 6 Months

About Us: Digital Alpha Platforms is a NY-based data and AI firm with a focus on financial services. In the realm of digital transformation, our distinctiveness lies in the seamless fusion of transformative solutions, cloud technologies, and a dynamic team comprising financial technology experts from prestigious institutions such as McKinsey, Bloomberg, Goldman Sachs, JP Morgan Chase, and Deloitte. As AWS partners with badges including AWS Financial Services Competency, AWS Data Analytics Competency, and Well-Architected Partner status, we harness the power of innovative cloud capabilities. This allows us not only to redefine current business operations but also to propel your enterprise toward a future marked by unrivalled digital excellence. Beyond tackling todays challenges, our commitment is to sculpt a path for your business that outpaces the expectations of tomorrows digital landscape.

Interns will support the design, prototyping, and deployment of generative AI solutions, focusing on:

• Architecting and implementing Retrieval-Augmented Generation (RAG) and Graph RAG pipelines to improve model retrieval and response quality .
• Developing AI agents that orchestrate multi-step tasks using LLMs, external tools, and knowledge graphs.

• Research & Development: Investigate and implement state-of-the-art RAG methods (text, graph, multi-modal) to enhance LLM performance.
• Pipeline Engineering: Build and optimize data pipelines for ingestion, preprocessing, fine-tuning, quantization, and deployment of transformer models at scale.
• Framework Integration: Integrate vector databases (e.g., Pinecone, Milvus) and graph-based retrieval systems using LangChain, LlamaIndex, or similar libraries.
• Collaboration & Documentation: Work alongside senior engineers and researchers to integrate AI components into applications, maintaining clear documentation and reproducibility.
• Evaluation & Reporting: Design experiments, benchmark model outputs, and present findings on metrics such as latency, relevance, and cost efficiency.

• Academic Background: Pursuing a Bachelors or Masters degree in Computer Science, Artificial Intelligence, Machine Learning, or a related quantitative discipline.
• Programming Skills: Proficient in Python; experience with ML frameworks like PyTorch or TensorFlow.
• Architectural Knowledge: Solid understanding of transformer architectures, embeddings, tokenizers, and prompt engineering.
• Tooling Experience: Familiarity with vector DBs, LangChain, LlamaIndex, and basic DevOps tools (Git, Docker, Kubernetes).
• Cloud & Deployment: Exposure to cloud platforms (AWS) for training and serving AI workloads.

• Multi-Modal RAG: Experience with text, image, audio, or graph-based retrieval for unified RAG systems.
• Knowledge Graphs & GNNs: Understanding of graph neural networks and knowledge-graph construction for graph-RAG applications.