Gen AI-ML Engineer – AVP

Role Overview

• Design, develop, and implement GenAI solutions for various financial applications, including personalized recommendations, risk assessment, fraud detection, and automated reporting.
• Explore and experiment with advanced GenAI concepts like Agentic AI.
• Design and implement intelligent chatbots.
• Process and analyze large datasets of structured and unstructured financial data.
• Architect and implement efficient RAG pipelines, leveraging tools like LlamaIndex and LangChain.
• Develop and refine advanced prompting strategies for LLMs.
• Test, evaluate, and analyze the performance of LLM and other GenAI models.
• Collaborate closely with engineering teams to deploy and maintain GenAI models in production environments, including containerization, CI/CD pipelines, and cloud infrastructure management.
• Communicate effectively with business stakeholders.
• Stay up-to-date with the latest advancements in GenAI research and development, including areas like Agentic AI.

Requirements

• 5 years+ of experience in AI/ML development, with a proven track record of building and deploying sophisticated GenAI applications.
• Deep understanding of GenAI models and architectures, including transformers, LLMs (e.g., Llama, Gemini, GPT-4), GANs, and diffusion models.
• Familiarity with Agentic AI concepts.
• Extensive experience with prompt engineering, fine-tuning LLMs, and evaluating their performance.
• Expert-level Python programming skills and proficiency with relevant libraries (e.g., Transformers, LangChain, TensorFlow, PyTorch, Pandas, NumPy, Scikit-learn, Flask/Django, LlamaIndex).
• Experience with vector databases (e.g., Pinecone, Weaviate, Chroma, Faiss, PostgreSQL with vector extensions) and implementing RAG pipelines using tools like LlamaIndex and LangChain.
• Strong software engineering skills, including containerization (Docker, Kubernetes), CI/CD pipelines, and cloud infrastructure management (AWS, Azure, GCP).
• Strong analytical, problem-solving, and communication skills.
• Experience with MLOps principles and tools.
• Excellent collaboration skills.

Tech Stack

• AWS
• Azure
• Cloud
• Django
• Docker
• Flask
• Google Cloud Platform
• Kubernetes
• Numpy
• Pandas
• Postgres
• Python
• PyTorch
• Scikit-Learn
• Tensorflow

Benefits

• medical, dental & vision coverage
• 401(k)
• life, accident, and disability insurance
• wellness programs
• paid time off packages, including planned time off (vacation), unplanned time off (sick leave), and paid holidays