Senior AI-ML Developer Lead

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We are looking for a Senior AI-ML Developer Lead : Location: Clinton, NJ (Hybrid) Position Type: 12+ Months Contract Position.

Role: Senior AI-ML Developer Lead

Customer: New York Life Insurance

Location / Work Mode: Clinton, NJ - Hybrid (3 days onsite per week)

Job Type: Contract

Duration: 6 months (subject to extension)

Experience Required: 14+ years

Role Overview

We are seeking a Senior AI-ML Developer Lead with deep enterprise experience in Generative AI, RAG pipelines, and Agent-based AI systems to lead design and delivery of next-generation AI solutions. This is a hands-on technical leadership role responsible for end-to-end architecture, development, deployment, and production support of AI/ML and GenAI platforms within a regulated enterprise environment.

The ideal candidate has evolved from strong software engineering foundations into senior AI leadership, with proven success delivering production-grade GenAI solutions at scale.

Key Responsibilities

AI / ML & Generative AI Leadership

• Provide end-to-end technical leadership across AI/ML and Generative AI initiatives, from ideation to production rollout.
• Define and own enterprise-level reference architectures for:
• Machine Learning platforms
• Generative AI solutions (RAG pipelines, agent frameworks)
• Data ingestion, feature engineering, and model orchestration
• Act as the GenAI / AI-ML Subject Matter Expert, setting engineering standards, design patterns, and best practices.
• Mentor and guide senior engineers through architecture reviews, code reviews, and technical decision-making.

Generative AI, RAG & Agent Systems

• Design and implement Retrieval-Augmented Generation (RAG) solutions including:
• Document ingestion and preprocessing
• Chunking and embedding strategies
• Vector databases and retrieval mechanisms
• Prompt engineering and response synthesis
• Build and deploy agent-based AI systems supporting:
• Multi-step reasoning
• Tool usage and orchestration
• Human-in-the-loop workflows
• Deliver GenAI use cases such as:
• Enterprise knowledge assistants
• Document intelligence and summarization
• Conversational AI and chatbots
• Workflow automation and decision support

Hands-on Development & Architecture

• Lead hands-on development using Python and modern backend frameworks (FastAPI, Flask).
• Build, train, fine-tune, and deploy ML and GenAI models using:
• PyTorch, TensorFlow, Scikit-learn
• Transformer-based LLM frameworks
• Implement scalable, containerized AI services using Docker and Kubernetes.
• Design CI/CD pipelines and deployment strategies for ML and GenAI workloads.

MLOps / GenAI Ops

• Establish and maintain MLOps and GenAI Ops practices including:
• Model lifecycle management
• Versioning, monitoring, and drift detection
• Prompt management and performance optimization
• Cost governance and scalability
• Work with cloud-native ML platforms and services.

Enterprise & Stakeholder Collaboration

• Collaborate with product, platform, data, risk, and business stakeholders to translate requirements into AI solutions.
• Lead technical design sessions, architecture reviews, and roadmap discussions.
• Ensure compliance with enterprise security, governance, and Responsible AI standards.

Required Experience & Skills

• 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 support.
• Proven experience delivering enterprise Generative AI solutions, including RAG pipelines and agent-based AI systems.
• Strong proficiency in Python and backend system design.
• Deep understanding of cloud-native architectures and scalable distributed systems.
• Experience working in regulated environments such as insurance, financial services, or banking.
• Strong leadership, mentoring, and cross-functional communication skills.

Preferred Qualifications

• Prior experience supporting large enterprise modernization programs.
• Familiarity with multiple LLM providers, vector databases, and GenAI platforms.
• Experience influencing AI strategy and architectural standards at enterprise scale.

Education

• Bachelor s or Master s degree in Computer Science, Engineering, Data Science, or a related field.