"Generative AI is reshaping how businesses create content and automate intelligence, making expertise in ML GenAI highly sought after. This contract role in Hartford offers a chance to work with cutting\u2011edge tools like Google Vertex AI while building scalable solutions. If you thrive on turning advanced models into production\u2011ready services, this opportunity could be your next big move.\n\n# Job Summary\nWe are seeking a seasoned ML GenAI Developer to design, develop, and deploy advanced AI solutions using Google Vertex AI and Python. The role involves building scalable backend services, optimizing large language models, and integrating AI capabilities via REST or gRPC APIs while collaborating with cross\u2011functional teams.\n\n# Top 3 Critical Skills Table\n| Skill | Why it's critical | Mastery Level |\n|-------|-------------------|--------------|\n| Generative AI with Vertex AI | Core platform for building and deploying LLM\u2011based solutions | Senior |\n| Python backend development | Enables creation of scalable ML workflows and services | Senior |\n| Model deployment & monitoring (REST/gRPC, cost efficiency) | Ensures production reliability, performance, and cost control | Senior |\n\n# Interview Preparation\n1. **How do you design a scalable ML pipeline on Vertex AI?**\n *What the interviewer is looking for:* Understanding of Vertex AI components, data preprocessing, training, hyperparameter tuning, and deployment strategies.\n2. **Explain the steps you take to evaluate and optimize a large language model for cost and performance.**\n *What the interviewer is looking for:* Knowledge of model benchmarking, latency profiling, quantization, distillation, and cost\u2011aware scaling.\n3. **Describe how you would implement data ingestion and feature engineering for a streaming data source.**\n *What the interviewer is looking for:* Experience with data pipelines (e.g., Dataflow, Pub/Sub), real\u2011time preprocessing, and feature store usage.\n4. **What are the trade\u2011offs between using REST APIs vs. gRPC for model serving?**\n *What the interviewer is looking for:* Insight into latency, payload size, streaming capabilities, and ecosystem compatibility.\n5. **How do you monitor model drift and trigger retraining in production?**\n *What the interviewer is looking for:* Strategies for drift detection, automated alerts, CI/CD pipelines, and version management.\n\n# Resume Optimization\n- Generative AI\n- Vertex AI\n- Python\n- ML workflows\n- Large Language Model (LLM) optimization\n- Data ingestion pipelines\n- Feature engineering\n- REST API integration\n- gRPC\n- Cost efficiency monitoring\n\n# Application Strategy\nWhen reaching out to the recruiter, send a concise email that starts with a friendly greeting, attaches your resume, and clearly highlights your top relevant skills. Make sure to mention related skills you possess, such as Generative AI with Vertex AI, Python backend development, and model deployment/monitoring. Reference specific projects where you built scalable ML pipelines or optimized LLMs, and explain how these experiences align with the responsibilities listed in the job description.\n\n# Career Roadmap\n| Current Role | Typical Experience | Core Focus | Next Position |\n|--------------|-------------------|------------|---------------|\n| ML GenAI Developer | 12+ years in ML/AI, strong Python & Vertex AI expertise | Build and deploy generative AI solutions | Senior ML Engineer |\n| Senior ML Engineer | 5\u20117 years leading AI projects | Architecture, performance tuning, team mentorship | Lead AI Architect |\n| Lead AI Architect | 8\u201110 years designing enterprise AI platforms | Strategy, cross\u2011team leadership, innovation | Director of AI |\n| Director of AI | 12+ years overseeing AI portfolios | Vision, governance, business impact | VP of AI / CTO |\n"