AI Foundational Model Engineer

VDart, Inc.

Jersey City, NJ, United States

22 days ago

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Role details

Contract type

Permanent contract

Employment type

Full-time (> 32 hours)

Experience level

Expert

Experience required

7 years minimum

Working hours

Regular working hours

Job source

www.careerjet.com

Tech stack

Application Programming Interfaces (APIs) Artificial Intelligence Amazon Web Services Microsoft Azure Cloud Engineering Continuous Integration Data as a Services Data Security Python (Programming Language) Machine Learning Open Source Technology Performance Tuning

+22 more

Release Management Cloud Services Tensorflow Runbook Search Technologies Software Deployment Software Engineering Pytorch Retrieval-Augmented Generation Transfer Learning Delivery Pipeline Large Language Models Model Validation Containerization AI Platforms Kubernetes Low Latency HuggingFace Machine Learning Operations Terraform Serverless Computing Databricks

Job description

Design, build, deploy, and optimize enterprise-grade AI systems powered by foundation models, LLMs, retrieval-augmented generation, and agentic workflows.
The role converts AI concepts into secure, scalable, observable, and supportable production systems on the enterprise AI-ready platform (AIRP), which is currently AWS-hosted while following a cloud-agnostic architecture blueprint.
Hands-on AWS AI and cloud engineering is a major asset because AIRP currently runs on AWS.
Candidates should be comfortable working with Terraform/IaC and CI/CD teams to move AI services and infrastructure through controlled deployment pipelines.
Experience should map to business AI use cases such as KYC, credit underwriting, pitch book generation, Banker 360, Customer 360, deal library intelligence, financial crime quality, and sanctions screening.
Primary ownership
Production LLM applications, RAG pipelines, AI services, and model-serving integrations for AIRP.
End-to-end LLMOps/MLOps lifecycle from experimentation to deployment, monitoring, evaluation, rollback, and continuous improvement.
Reusable AI service components, APIs, prompts, retrieval logic, and observability patterns that can be federated across multiple business use cases.
Key responsibilities
Design and implement LLM-powered applications such as knowledge assistants, document intelligence solutions, workflow agents, summarization tools, and decision-support systems.
Build RAG pipelines using embeddings, chunking strategies, vector databases, semantic retrieval, reranking, response grounding, and citation patterns.
Integrate AI capabilities with AWS-hosted platform components, including model APIs, model gateways, data services, container platforms, and enterprise authentication patterns.
Collaborate with cloud engineering teams on Terraform modules, IaC templates, environment promotion, CI/CD pipelines, release controls, and rollback procedures.
Adapt and optimize models using LoRA, PEFT, instruction tuning, distillation, transfer learning, quantization, and domain adaptation techniques where appropriate.
Optimize inference workloads for latency, throughput, token efficiency, cost, reliability, and user experience.
Implement model and application observability, including prompt logs, retrieval quality, hallucination indicators, drift signals, feedback loops, cost telemetry, and service health.
Embed security, privacy, Responsible AI, and model risk controls into AI application design and delivery.
Create production documentation, runbooks, release notes, test evidence, and audit-ready implementation records., * Banking, risk, compliance, financial crime, operations, or enterprise technology background.
Experience with AWS Bedrock, SageMaker, OpenSearch, Kendra, Lambda, EKS/ECS, Azure OpenAI, Vertex AI, Databricks, vLLM, Triton, MLflow, Kubeflow, or model gateways.
Exposure to cloud-agnostic application patterns, reusable IaC modules, model risk, AI governance, audit controls, AI cost governance, and private or open-source LLM deployments.
Initial screening questions
Describe a production LLM or RAG system you built. What was your role and what changed after launch?
Which AWS AI or cloud services have you used for production AI delivery, and what design trade-offs did you make?
How have you worked with Terraform, IaC modules, or CI/CD pipelines to deploy AI services?
How did you evaluate groundedness, hallucination rate, retrieval quality, latency, and cost?
How did you secure sensitive data and prevent leakage in the AI pipeline?
What observability and rollback mechanisms did you implement?, Lead Machine Learning Engineer (MLOps, KServe + building Kubernetes Clusters, PyTorch, TensorFlow on AWS) As a Capital One Machine Learning Engineer (MLE), you’ll be part of an Agi…
1 day ago +

Requirements

7+ years in AI/ML engineering, platform engineering, software engineering, or applied machine learning.
Hands-on experience with LLMs, transformers, embeddings, RAG, semantic search, and GenAI application patterns.
Strong Python engineering skills with PyTorch, TensorFlow, Hugging Face, LangChain, LlamaIndex, Semantic Kernel, or equivalent frameworks.
Experience deploying production AI services using APIs, containers, Kubernetes, CI/CD, cloud-native services, and monitoring platforms.
Practical exposure to AWS AI/cloud services or comparable cloud-native AI deployment experience, with ability to ramp quickly on AWS-hosted AIRP patterns.
Working knowledge of Terraform/IaC, DevOps pipelines, release management, model evaluation, inference optimization, and secure data handling.