GenAI Architect – AI/ML & Data Platforms

GenAI Architect – AI/ML & Data Platforms

Location: Mount Laurel, NJ (Hybrid)

Duration: 12 Months (W2)

Experience: 15+ Years

Visa: -EAD, TN, GC-EAD, H1B Transfer

Interview Process: Video – Multiple Rounds

Role Overview

We are seeking an experienced GenAI Architect with deep expertise in building and deploying generative AI solutions at scale. The ideal candidate will lead the design, development, and optimization of large language models (LLMs), AI pipelines, and enterprise-grade AI/ML systems.

This role requires strong hands-on experience with open-source and commercial LLMs, cloud platforms (Azure), and modern MLOps practices. The candidate will collaborate with cross-functional teams to translate business requirements into scalable AI-driven solutions while ensuring performance, reliability, and continuous improvement.

Key Responsibilities

·         Design, develop, and optimize LLMs, multimodal models, and generative AI pipelines for business use cases

·         Build scalable AI/ML systems, including data pipelines, model training workflows, and inference services

·         Evaluate and integrate open-source and commercial LLMs (e.g., GPT, Llama, Claude, Mistral)

·         Implement Retrieval-Augmented Generation (RAG), vector databases, embeddings, and prompt engineering techniques

·         Collaborate with product, engineering, and business teams to deliver AI-powered solutions

·         Conduct experimentation, benchmarking, and A/B testing to evaluate model performance

·         Deploy AI/ML models using Azure cloud services and MLOps tools

·         Monitor, troubleshoot, and continuously enhance AI systems in production environments

·         Mentor and guide junior team members as needed

Technical Skills

Primary Skills

·         Generative AI / LLMs (Open-source and commercial models)

·         Python programming

·         Azure Cloud Platform

·         Databricks

Secondary Skills

·          Data Warehousing concepts

Additional Technical Expertise

·          Hands-on experience with LLMs, transformers, and generative AI architectures

·          Experience with prompt engineering, fine-tuning, and model evaluation

·          Familiarity with MLOps tools such as MLflow, Kubeflow, Docker, and Kubernetes

·          Experience with agentic frameworks like LangChain, LlamaIndex, or similar

·          Knowledge of multimodal AI (vision + language models)

·          Strong data engineering experience with Azure (ADF, Synapse, PySpark/Python)

·          Understanding of source-to-target mapping (STTM) and technical documentation

ELK Stack & Observability

·          Expertise in Elasticsearch, Logstash, and Kibana (ELK Stack)

·          Experience with Kibana visualization tools (Lens, Maps, Graph)

·          Proficiency in Kibana Query Language (KQL) and Elasticsearch aggregations

·          Experience building monitoring and observability dashboards

·          Knowledge of JSON and REST APIs for Elasticsearch integration

·          Familiarity with Elasticsearch ecosystem tools such as Beats

Security, Domain & Additional Requirements

·          Ability to work in hybrid environment from client location

·          Strong problem-solving and collaboration skills

·          Experience in banking domain (Risk & Regulatory, Commercial, or Credit Cards/Retail) – Nice to Have

·          Relevant certifications – Nice to Have

Required Qualifications

·          15+ years of experience in AI/ML, data engineering, or related fields

·          Proven experience designing and deploying enterprise-scale AI solutions

·          Strong programming expertise in Python and experience with ML frameworks

·          Hands-on experience with cloud-based AI/ML deployments and MLOps practices

·          Excellent communication and leadership skills