Gen AI Engineering Development Senior Lead

Key Skills

Aartificial intelligence, AWS, Continuous Integration & Continuous Delivery - CI/CD, Docker, HuggingFace, Kubernetes-K8s, Machine learning techniques, Natural Language Processing (NLP), Python Programming

Job Description

Gen Al Engineering and Development Lead will design and implement advanced Al solutions within the financial services sector. This role involves leveraging deep understanding of the range of state-of-the-art generative Al models, robust cloud infrastructure, MLOps principles, prompt engineering, safety mechanisms, model evaluation, and human-in-the-loop (HIL) frameworks to architect and deliver transformative applications. Be part of shaping the future of financial services technology by leading the development of innovative, responsible Al solutions.

ESSENTIAL DUTIES AND RESPONSIBILITIES

Following is a summary of the essential functions for this job. Other duties may be performed, both major and minor, which are not mentioned below. Specific activities may change from time to time. 

Model Development & Integration:

• Build, optimize, and deploy a versatile toolkit of generative language models, including open-source options and leading commercial offerings (OpenAl's GPT-3/4, Anthropic's Claude, Google's Gemini, Meta's LLaMA, Hugging Face models, etc.).
• Fine-tune these models for specialized financial service tasks (market analysis summarization, intelligent Q&A for regulatory compliance, fraud detection, risk assessment report generation, and code generation, etc. ).

Prompt Engineering:

• Craft effective prompt templates and engage in prompt engineering strategies to maximize the potential and performance of generative models.

Al Agents Development:

• Design and build intelligent conversational and other types of Al agents that leverage generative Al models for advanced interactions within the financial services domain.
• Optimize agent capabilities for tasks such as personalized recommendations, complex question answering, and the execution of multistep processes, ensuring a seamless and engaging user experience.

Full-Stack Generative Al Mastery:

• Design and implement robust APls with clearly defined endpoints, ensuring seamless interaction between your generative Al models and existing financial systems.
• Integrate these models into a scalable production environment, leveraging the power of AWS (such as SageMaker and Bedrock as a plus) and your experience with RAG (retrieval augmented generation) techniques for efficient information handling.

Safety & Guardrails:

• Develop and integrate guardrails and related APls to mitigate risks, ensure responsible use of generative models, and align output with ethical Al principles.

Model Evaluation & Human-in-the-Loop Integration:

• Design and implement robust model evaluation techniques, incorporating human expertise through effective Human-in-the-Loop (HIL) workflows.
• Utilize embedding workflows within the HIL framework to ensure human evaluators can effectively assess the quality, fairness, and alignment of model outputs with financial domain requirements.

Industry Specialization:

• Apply your deep understanding of the financial services industry to tailor generative Al solutions that solve critical challenges and unlock new opportunities.

POC Leadership:

• Drive the development of hands-on proof-of-concept (POC) projects, demonstrating the value and feasibility of innovative generative Al use cases within the financial domain.

QUALIFICATIONS

Required Qualifications:

The requirements listed below are representative of the knowledge, skill and/or ability required. Reasonable accommodations may be made to enable individuals with disabilities to perform the essential functions.

• Education: Bachelor's degree in Computer Science, Software Engineering, or a related field

• Experience: Minimum of 4+ years of hands-on experience building and deploying applications leveraging natural language processing (NLP) and generative Al models

• Minimum of 10+ years of demonstrated experience with financial services datasets and use cases

• Hands-on experience developing and leading proof-of-concept (POC) projects

Preferred Qualifications:

• Experience with crafting prompt templates and prompt engineering strategies.

• Experience in developing guardrails, safety mechanisms, or related APls for generative Al solutions.

• Experience with model evaluation techniques, including incorporating human expertise through Human-in-the-Loop (HIL) workflows.

• Deep proficiency in Python, and relevant libraries/frameworks (Transformers, Hugging Face, etc.).

• Experience with AWS (SageMaker is critical, Bedrock a plus), containerization (Docker, Kubernetes), and Cl/CD.

• Proven experience working with different LLM providers (AWS, OpenAI, Anthropic, Google, Meta, etc.).

• Experience with API design, including creating well-defined endpoints.

• Experience with RAG (retrieval augmented generation) techniques