AI Foundational Model Engineer

Compunnel Inc.; Jersey City (NJ); On-site; USD 150,000 - 230,000; Full time

Job summary

Compunnel, Inc. is seeking an experienced AI/ML engineer to translate AI concepts into secure, scalable production systems on the AIRP platform. You will design and implement LLM-powered applications, build RAG pipelines, and integrate with AWS platform components to deliver enterprise AI solutions.

The role requires strong Python and AI deployment experience, collaboration with cloud engineering, and a focus on observability, security, and governance throughout the lifecycle.

Qualifications

• 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.

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.

Job description

JOB SUMMARY

This role converts AI concepts into secure, scalable, observable, and supportable production systems on the enterprise AI-ready platform (AIRP).

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.

Required Qualifications

• 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.

Preferred Qualifications

• 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.