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.
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