Senior AI/ML Developer Lead - 14+ Years

Hi,

Job Title: Senior AI/ML Developer Lead

Location: Clinton, NJ - 3 Days Onsite

Duration: 6+ Months Contract

Note:

Customer expectation – Gen AI / RAG/Agent Core

Experience Required

• 14+ years of overall software engineering experience with progression into senior technical leadership roles.
• 8+ years of hands-on AI/ML experience, including model development, deployment, and production operations.
• Proven enterprise experience delivering Generative AI solutions, including RAG pipelines and agent-based AI systems, end to end.

Job Description:

About the Role

We are seeking a Senior AI/ML Developer Lead with 14+ years of overall software engineering experience to lead enterprise AI and Generative AI modernization initiatives under the CoreMod program. This is a hands-on senior leadership role that combines deep technical expertise, architectural ownership, and team leadership to modernize legacy platforms and deliver production-grade AI, GenAI, RAG, and Agentic AI solutions at enterprise scale.

This role is ideal for a seasoned technologist who has evolved from strong backend or full-stack engineering into AI/ML and GenAI leadership, and who has successfully delivered secure, scalable AI systems in regulated environments such as financial services, banking, or insurance.

You will serve as a technical authority and thought leader, closing SME gaps, setting architecture standards, mentoring teams, and driving adoption of cloud-native ML platforms, Retrieval-Augmented Generation (RAG), and agent-based AI systems.

What You'll Be Doing:

AI/ML, GenAI & Engineering Leadership

• Provide end-to-end technical leadership across the AI/ML and Generative AI lifecycle — from use-case design and architecture through production deployment, optimization, and monitoring.
• Own and define reference architectures for:
• Enterprise ML platforms
• Generative AI solutions including RAG pipelines and agentic AI workflows
• Scalable data and feature pipelines supporting CoreMod modernization
• Act as the primary AI/ML and GenAI SME, establishing engineering standards, design patterns, and best practices across teams.
• Lead and mentor ML engineers and developers through design reviews, code reviews, and architectural governance, ensuring solutions are secure, scalable, and production-ready.

Enterprise AI & GenAI Modernization

• Lead modernization of legacy analytics, decisioning, and rule-based systems by introducing cloud-native ML, GenAI, and microservices architectures.
• Design and deliver batch and real-time ML systems, including forecasting engines, risk and propensity models, and intelligent automation workflows.
• Drive adoption of Generative AI and RAG for:
• Document intelligence and knowledge extraction
• Enterprise search and knowledge assistants
• Conversational AI and internal/external chatbots
• Workflow and decision automation
• Design and implement agent-based AI systems leveraging agent orchestration frameworks to enable multi-step reasoning, tool usage, and human-in-the-loop controls.
• Ensure all solutions meet enterprise security, compliance, model governance, and responsible AI standards, particularly in regulated environments.

Hands-on Development & Architecture Ownership

• Contribute directly to hands-on development using Python and modern backend frameworks such as FastAPI and Flask, building scalable AI/ML and GenAI services.
• Build, fine-tune, and deploy models using PyTorch, TensorFlow, Scikit-learn, LightGBM, XGBoost, and transformer-based LLM frameworks.
• Design and optimize RAG architectures, including document ingestion, chunking strategies, embedding generation, retrieval, re-ranking, and response synthesis.
• Implement and scale agentic AI workflows using frameworks such as Autogen, MCP, LangGraph, CrewAI, or equivalent.
• Build data ingestion, feature engineering, and orchestration pipelines using Spark, Snowflake, Databricks, Airflow, and cloud-native services.
• Lead containerized deployments using Docker, Kubernetes, Helm, and manage CI/CD pipelines with Jenkins, GitHub Actions, or similar tools.
• Establish and govern MLOps / GenAI Ops practices using MLflow, Kubeflow, SageMaker, including model/version control, prompt management, monitoring, drift detection, and cost optimization.

Cross-Functional & Stakeholder Collaboration

• Partner closely with product, data, platform, risk, and business teams to translate complex business problems into scalable AI, GenAI, and agent-based solutions.
• Lead technical design sessions, architecture reviews, and roadmap planning with senior internal stakeholders.
• Support agile delivery through technical backlog grooming, sprint planning, estimation, and delivery oversight.

Vendor, Platform & Ecosystem Enablement

• Evaluate, onboard, and integrate external AI platforms, LLM providers, vector databases, and vendor solutions.
• Lead technical PoCs, architecture assessments, and deep-dive evaluations of third-party tools and platforms.
• Influence platform strategy and long-term technology choices aligned with enterprise AI vision.

Communication, Influence & Thought Leadership

• Clearly articulate AI/ML, GenAI, RAG, and agent architectures, trade-offs, and business outcomes to executive, technical, and non-technical audiences.
• Produce high-quality technical documentation, architecture diagrams, standards, and delivery artifacts.

Serve as a trusted technical advisor, influencing decision-making and building confidence in AI-led modernization initiatives

Why Join Us:

• Be part of a rapidly rising, global technology innovator whose platforms and services are engaged by Fortune 1000 companies and industry leaders such as Microsoft & Amazon.
• Immerse yourself in a culture where creativity is celebrated and encouraged.
• Engage in thrilling projects and opportunities for your professional growth.
• Contribute to our quest to redefine the industry.
• Competitive compensation and benefits with occasional ‘distinctive benefits’ that set us apart.

Who you are:

Experience Required

• 14+ years of overall software engineering experience with progression into senior technical leadership roles.
• 8+ years of hands-on AI/ML experience, including model development, deployment, and production operations.
• Proven enterprise experience delivering Generative AI solutions, including RAG pipelines and agent-based AI systems, end to end.
• Strong experience in financial services, insurance, banking, or other regulated environments.
• Deep expertise in Python, backend development, cloud-native architectures, and modern DevOps/MLOps practices.
• Demonstrated ability to lead, mentor, and influence senior engineers and cross-functional stakeholders.
• Prior experience in large, complex enterprise environments (e.g., NYL or similar) is a strong plus.

Education:

• Bachelor’s or master’s degree in computer science, Engineering, Data Science, Statistics, or related field.