AI / ML Software Engineer 

AI / ML Software Engineer 
Client: Vanguard | Location: Malvern, PA (Onsite)
Title: AI / ML Software Engineer ( Banking Clients) 

Need Genuine Candidates only H1B Visa Applicable 

Role Summary
Hands-on engineering leader for production-grade ML / LLM / GenAI services. Set technical direction, remain close to code/architecture, establish delivery & engineering standards, and partner with stakeholders (technical + non-technical). Mentor junior engineers; build institutional MLOps (deployment, monitoring, model lifecycle, scalability).

Key Responsibilities (Important Points)
Design → Deploy: Build and run production ML/LLM/GenAI solutions; own reliability/operability after deployment.
Stakeholder Partnership: Explain what is built, why it matters, and production performance to both technical and non-technical audiences.
Mentorship & Standards: Conduct design/code reviews, pairing, coaching; raise engineering quality across workstreams.
MLOps: Institutionalize automated pipelines for deployment, monitoring, and model lifecycle management; emphasize scalability/reliability.
NLP / Generative AI: Fine-tune generative models for NLP (summarization, text generation); evaluate models (e.g., GPT-4.1); iterate architectures for performance.
Monitoring: Implement real-time tracking of model performance and reliability.
Innovation: Stay current with AI/ML/LLM/GenAI research; leverage external APIs (OpenAI) for enhanced functionality.
Collaboration: Work with product managers, data scientists, ML engineers; prioritize use cases.

Required Qualifications (Must-Have)
Education: Bachelor’s / Master’s in CS, Engineering, or related field.
Experience: Proven engineering experience building/deploying/operating applied AI/ML in production (model lifecycle, MLOps, monitoring, governance).
Leadership: Hands-on engineering leadership — architecture decisions, technical direction, mentoring, guiding quality across multiple workstreams.
Languages / APIs: Python for model dev, experimentation, integration; OpenAI API.
Frameworks: TensorFlow, PyTorch, Scikit-learn; OpenAI API.
Infrastructure: Cloud (AWS / Azure / GCP); containerization (Docker / Kubernetes); microservices design/optimization.
Fundamentals: Statistics, ML (classification, regression, time series, deep learning, RL), generative architectures (GANs, VAEs).
Optimization: Identify AI/ML/LLM/GenAI challenges; fine-tune models for optimal NLP performance.
Portfolio: Successful GenAI NLP applications using OpenAI APIs for prompt engineering.
Soft Skills: Strong collaboration; communicate complex concepts to cross-functional/interdisciplinary teams.

Preferred (Nice-to-Have — Keep if Relevant)
Industry: Financial services familiarity (Client = Vanguard).
RAG: Design/implement Retrieval-Augmented Generation (RAG) pipelines.
Advanced Prompting: Chain-of-Thoughts, Tree-of-Thoughts, Graph-of-Thoughts prompting strategies.