Artificial Intelligence Specialist

V2Soft•Dearborn, MI

9h•Hybrid

About The Position

V2Soft is a global leader in IT services and business solutions, delivering innovative and cost-effective technology solutions worldwide since 1998. We have headquartered in Bloomfield Hills, MI and have 16 offices spread across six countries. We partner with Fortune 500 companies to address complex business challenges. Our services span AI, IT staffing, cloud computing, engineering, mobility, testing, and more. Certified with CMMI Level 3 and ISO standards, V2Soft is committed to quality and security. Beyond our work, we actively support local communities and non-profits, reflecting our core values. Join us to be part of a dynamic and impactful global company! Please visit us at www.V2soft.com to know more. Only W2, No C2C - 4 Days onsite at Dearborn, MI

Requirements

2–5 years of Technical Communication experience translating complex technical concepts into clear documentation, proposals, and presentations for both technical and non-technical audiences.
2–5 years of Communications experience demonstrating ability to communicate effectively across cross-functional teams, facilitating technical discussions, contributing to design reviews, and keeping stakeholders aligned.
2–5 years of hands-on experience with GCP services relevant to AI/ML and data workloads, including Vertex AI, BigQuery, GCS, Dataflow, or Cloud Composer.
2–5 years of experience building, training, and evaluating machine learning models using TensorFlow or TensorFlow Extended (TFX).
2–4 years of experience applying data governance principles including data lineage, access controls, metadata management, and compliance standards.
3–5 years of applied ML experience including feature engineering, model selection, training, validation, and deployment.
3–5 years of writing production-quality Python for data engineering, ML pipeline development, or platform tooling. Proficiency with relevant libraries such as Pandas, NumPy, scikit-learn, and TensorFlow is expected.
3–5 years of experience designing or working with AI systems, including the application of large language models, expert systems, or intelligent automation.
5 or more years of professional experience in machine learning engineering, AI systems development, or applied AI research.
Hands-on experience fine-tuning LLMs in a cloud environment, with specific preference for Google Cloud Vertex AI or equivalent managed ML platforms.
Demonstrated experience building agentic AI systems using frameworks such as LangChain, LangGraph, Google Agent Builder, or equivalent orchestration tooling.
Proficiency in Python and ML development tooling including Hugging Face, PyTorch or TensorFlow, and MLflow or Vertex AI Experiments.
Experience designing and evaluating LLM outputs for production systems, including prompt engineering, retrieval-augmented generation (RAG) architectures, and model evaluation metrics.
Strong understanding of MLOps practices including model versioning, deployment pipelines, monitoring, and retraining workflows on GCP.
Experience working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns.
Strong written and verbal communication skills.

Nice To Haves

Experience with automotive telemetry use cases.

Responsibilities

Translating complex technical concepts — such as ML model behavior, data pipeline architecture, or platform design decisions — into clear documentation, proposals, and presentations for both technical and non-technical audiences including engineering leads and product stakeholders.
Communicating effectively across cross-functional teams, including facilitating technical discussions, contributing to design reviews, and keeping stakeholders aligned on project status, risks, and decisions.
Deploying and managing workloads in a production cloud environment using Google Cloud Platform (GCP) services relevant to AI/ML and data workloads, including Vertex AI, BigQuery, GCS, Dataflow, or Cloud Composer.
Building, training, and evaluating machine learning models using TensorFlow or TensorFlow Extended (TFX), including experience with model versioning, pipeline integration, and deploying models to production serving infrastructure.
Applying data governance principles including data lineage, access controls, metadata management, and compliance standards to ensure telemetry and ML datasets meet quality, security, and regulatory requirements.
Performing applied ML tasks including feature engineering, model selection, training, validation, and deployment, working with both structured and unstructured data in the context of real-world engineering or automotive telemetry use cases.
Writing production-quality Python for data engineering, ML pipeline development, or platform tooling, utilizing libraries such as Pandas, NumPy, scikit-learn, and TensorFlow, and adhering to code quality practices like testing and version control.
Designing or working with AI systems, including the application of large language models, expert systems, or intelligent automation within developer or data workflows, understanding model lifecycle management, prompt engineering, and responsible AI practices.
Fine-tuning LLMs in a cloud environment, with specific preference for Google Cloud Vertex AI or equivalent managed ML platforms.
Building agentic AI systems using frameworks such as LangChain, LangGraph, Google Agent Builder, or equivalent orchestration tooling.
Designing and evaluating LLM outputs for production systems, including prompt engineering, retrieval-augmented generation (RAG) architectures, and model evaluation metrics.
Implementing MLOps practices including model versioning, deployment pipelines, monitoring, and retraining workflows on GCP.
Working in regulated or IP-sensitive environments where model artifact ownership and data governance are active concerns.
Translating technical AI concepts for non-technical executive stakeholders.